Star Research (ስታር - ሪሰርች）

A brief mention of the important sample designs is as follows: 👍a. Deliberate sampling: Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves purposive or deliberate selection of particular units of the universe for constituting a sample which represents the universe. When population elements are selected for inclusion in the sample based on the ease of access, it can be called convenience sampling. 👍b. Simple random sampling: This type of sampling is also known as chance sampling or probability sampling where each and every item in the population has an equal chance of inclusion in the sample and each one of the possible samples, in case of finite universe, has the same probability of being selected. 👍c.Systematic sampling: In some instances the most practical way of sampling is to select every 15th name on a list, every 10th house on one side of a street and so on. Sampling of this type is known as systematic sampling. An element of randomness is usually introduced into this kind of sampling by using random numbers to pick up the unit with which to start. This procedure is useful when sampling frame is available in the form of a list. 👍d.Stratified sampling: If the population from which a sample is to be drawn does not constitute homogeneous group, then stratified sampling technique is applied so as to obtain representative sample. In this technique, the population is stratified into a number of non-overlapping subpopulations or strata and sample items are selected from each stratum. 👍e. Quota sampling: In stratified sampling the cost of taking random samples from individual strata is often so expensive that interviewers are simply given quota to be filled from different strata, the actual selection of items for sample being left to the interviewer judgment. 👍f. Cluster sampling and area sampling: Cluster sampling involves grouping the population and then selecting the groups or the clusters rather than individual elements for inclusion in the sample. Suppose some departmental store wishes to sample its credit card holders. 👍g. Multi-stage sampling: This is a further development of the idea of cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an entire country. Under multi-stage sampling the first stage may be to select large primary sampling units such as states, then districts, then towns and finally certain families within towns. 👍h. Sequential sampling: This is somewhat a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions on the basis of information yielded as survey progresses. This design is usually adopted under acceptance sampling plan in the context of statistical quality control. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️           +251912688642          +251912688642 Telegram Account           https://t.me/Research100stock           https://t.me/Research100stock

Identification of a Research Problem 🍏The following steps are to be followed in identifying a research problem; Step I Determining the field of research in which a researcher is keen to do the research work. Step II The researcher should develop the mastery on the area or it should be the field of his specialization. Step III He/She should review the research conducted in area to know the recent trend and studies are being conducted in the area. Step IV On the basis of review, he/she should consider the priority field of the study. Step V He/She should draw an analogy and insight in identifying a problem or employ his personal experience of the field in locating the problem. He/She may take help of supervisor or expert of the field. Step VI He/She should pin point specific aspect of the problem which is to be investigated If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️           +251912688642          +251912688642 Telegram Account           https://t.me/Research100stock           https://t.me/Research100stock

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The main characteristics of a good sample ✍️ A good sample is the true representative of the population corresponding to its properties. The population is known as aggregate of certain properties and sample is called sub-aggregate of the universe. ✍️A good sample is free from bias, the sample does not permit prejudices th65e learning and pre- conception, imaginations of the investigator to influence its choice. ✍️A good sample is an objective one, it refers objectivity in selecting procedure or absence of subjective elements from the situation. ✍️ A good sample maintains accuracy. It yields an accurate estimates or statistics and does not involve errors. ✍️A good sample is comprehensive in nature. This feature of a sample is closely linked with true-representativeness. Comprehensiveness is a quality of a sample which is controlled by specific purpose of the investigation. A sample may be comprehensive in traits but may not be a good representative of the population. ✍️ A good sample is also economical from energy, time and money point of view. ✍️ The subjects of good sample are easily approachable. The research tools can be administered on them and data can be collected easily. ✍️The size of good sample is such that it yields an accurate results. The probability of error can be estimated. ✍️ A good sample makes the research work more feasible. ✍️ A good sample has the practicability for research situation. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️           +251912688642          +251912688642 Telegram Account           https://t.me/Research100stock           https://t.me/Research100stock

Reliability and validity 🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool. ✍️Reliability 👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions. Types of Reliability:     👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times.    👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers.     👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha).     👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test. How to Test Reliability:     🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability).     🌱A reliability coefficient of 0.7 or higher is generally considered acceptable. 🌎Validity 👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure. Types of Validity:          🤙Content Validity: Ensures the test covers all aspects of the concept being measured.          🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure.          🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same                  construct.          🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs.          🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion.         🤙  Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously.          🤙 Predictive Validity: Assesses how well the test predicts future outcomes. How to Test Validity:    ✏️ Use expert reviews for content validity.     ✏️ Conduct factor analysis or correlation studies for construct validity.     ✏️ Compare the test results with an established criterion for criterion validity. 🍏Key Differences:     💎Reliability is about consistency (does the test produce stable results?).     💎Validity is about accuracy (does the test measure what it claims to measure?). If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

Classification of Research based on Application: 🍏a. Pure / Basic / Fundamental Research: As the term suggests a research activity taken up to look into some aspects of a problem or an issue for the first time is termed as basic or pure. It involves developing and testing theories and hypotheses that are intellectually challenging to the researcher but may or may not have practical application at the present time or in the future. The knowledge produced through pure research is sought in order to add to the existing body of research methods. Pure research is theoretical but has a universal nature. It is more focused on creating scientific knowledge and predictions for further studies. 🍏b. Applied / Decisional Research: Applied research is done on the basis of pure or fundamental research to solve specific, practical questions; for policy formulation, administration and understanding of a phenomenon. It can be exploratory, but is usually descriptive. The purpose of doing such research is to find solutions to an immediate issue, solving a particular problem, developing new technology and look into future advancements etc. This involves forecasting and assumes that the variables shall not change. Key Differences between Basic and Applied Research ✍️a) Basic Research can be explained as research that tries to expand the already existing scientific knowledge base. On the contrary, applied research is used to mean the scientific study that is helpful in solving real-life problems. ✍️b) While basic research is purely theoretical, applied research has a practical approach. ✍️c) The applicability of basic research is greater than the applied research, in the sense that the former is universally applicable whereas the latter can be applied only to the specific problem, for which it was carried out. ✍️d) The primary concern of the basic research is to develop scientific knowledge and predictions. On the other hand, applied research stresses on the development of technology and technique with the help of basic science. ✍️e) The fundamental goal of the basic research is to add some knowledge to the already existing one. Conversely, applied research is directed towards finding a solution to the problem under consideration. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

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Research design 👍Research design is a comprehensive plan for data collection in an empirical research project. It is a “blueprint” for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: (1) the data collection process, (2) the instrument development process, and (3) the sampling process. 🍏Broadly speaking, data collection methods can be broadly grouped into two categories: positivist and interpretive. 👍 Positivist methods, such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. 👍In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

Reliability and validity 🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool. ✍️Reliability 👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions. Types of Reliability:     👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times.    👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers.     👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha).     👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test. How to Test Reliability:     🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability).     🌱A reliability coefficient of 0.7 or higher is generally considered acceptable. 🌎Validity 👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure. Types of Validity:          🤙Content Validity: Ensures the test covers all aspects of the concept being measured.          🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure.          🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same                  construct.          🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs.          🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion.         🤙  Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously.          🤙 Predictive Validity: Assesses how well the test predicts future outcomes. How to Test Validity:    ✏️ Use expert reviews for content validity.     ✏️ Conduct factor analysis or correlation studies for construct validity.     ✏️ Compare the test results with an established criterion for criterion validity. 🍏Key Differences:     💎Reliability is about consistency (does the test produce stable results?).     💎Validity is about accuracy (does the test measure what it claims to measure?). If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

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The main characteristics of a good sample ✍️ A good sample is the true representative of the population corresponding to its properties. The population is known as aggregate of certain properties and sample is called sub-aggregate of the universe. ✍️A good sample is free from bias, the sample does not permit prejudices th65e learning and pre- conception, imaginations of the investigator to influence its choice. ✍️A good sample is an objective one, it refers objectivity in selecting procedure or absence of subjective elements from the situation. ✍️ A good sample maintains accuracy. It yields an accurate estimates or statistics and does not involve errors. ✍️A good sample is comprehensive in nature. This feature of a sample is closely linked with true-representativeness. Comprehensiveness is a quality of a sample which is controlled by specific purpose of the investigation. A sample may be comprehensive in traits but may not be a good representative of the population. ✍️ A good sample is also economical from energy, time and money point of view. ✍️ The subjects of good sample are easily approachable. The research tools can be administered on them and data can be collected easily. ✍️The size of good sample is such that it yields an accurate results. The probability of error can be estimated. ✍️ A good sample makes the research work more feasible. ✍️ A good sample has the practicability for research situation. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️           +251912688642          +251912688642 Telegram Account           https://t.me/Research100stock           https://t.me/Research100stock

🍎Panel logistic regression🍏 👍Panel logistic regression, also known as fixed-effects logistic regression or conditional logistic regression, is a statistical method used to analyze panel or longitudinal data where the dependent variable is binary (i.e., it takes on two possible values) and there are repeated observations on the same individuals over time. 👍Panel data refers to a dataset that contains observations on the same individuals or entities over multiple time periods. Examples of panel data include tracking individuals' health outcomes over time, analyzing financial data of companies over several years, or examining the voting behavior of individuals across multiple elections. 👍Logistic regression, on the other hand, is a statistical model used to estimate the probability of a binary outcome based on one or more independent variables. It is commonly used when the dependent variable is dichotomous, such as predicting whether a customer will churn or not, or whether a patient will respond to a particular treatment or not. 👍Panel logistic regression extends logistic regression to account for the panel structure of the data. It incorporates fixed effects, which capture individual-specific heterogeneity that is constant over time but may affect the outcome variable. By including fixed effects, panel logistic regression controls for unobserved individual-level characteristics that may be correlated with the dependent variable. 👍The fixed effects in panel logistic regression are typically included as dummy variables for each individual in the panel. These fixed effects capture the individual-specific intercepts and allow for the estimation of time-varying effects of the independent variables on the dependent variable. 👍Panel logistic regression can provide insights into how individual characteristics and time-varying factors influence the probability of the binary outcome. It is often used in various fields, including economics, social sciences, and public health, to analyze longitudinal data and understand the determinants of binary outcomes over time. If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️           +251912688642          +251912688642 Telegram Account           https://t.me/Research100stock           https://t.me/Research100stock

Reliability and validity 🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool. ✍️Reliability 👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions. Types of Reliability:     👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times.    👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers.     👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha).     👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test. How to Test Reliability:     🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability).     🌱A reliability coefficient of 0.7 or higher is generally considered acceptable. 🌎Validity 👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure. Types of Validity:          🤙Content Validity: Ensures the test covers all aspects of the concept being measured.          🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure.          🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same                  construct.          🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs.          🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion.         🤙  Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously.          🤙 Predictive Validity: Assesses how well the test predicts future outcomes. How to Test Validity:    ✏️ Use expert reviews for content validity.     ✏️ Conduct factor analysis or correlation studies for construct validity.     ✏️ Compare the test results with an established criterion for criterion validity. 🍏Key Differences:     💎Reliability is about consistency (does the test produce stable results?).     💎Validity is about accuracy (does the test measure what it claims to measure?). If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

Reliability and validity 🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool. ✍️Reliability 👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions. Types of Reliability: 👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times. 👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers. 👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha). 👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test. How to Test Reliability: 🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability). 🌱A reliability coefficient of 0.7 or higher is generally considered acceptable. 🌎Validity 👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure. Types of Validity: 🤙Content Validity: Ensures the test covers all aspects of the concept being measured. 🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure. 🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same construct. 🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs. 🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion. 🤙 Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously. 🤙 Predictive Validity: Assesses how well the test predicts future outcomes. How to Test Validity: ✏️ Use expert reviews for content validity. ✏️ Conduct factor analysis or correlation studies for construct validity. ✏️ Compare the test results with an established criterion for criterion validity. 🍏Key Differences: 💎Reliability is about consistency (does the test produce stable results?). 💎Validity is about accuracy (does the test measure what it claims to measure?). If you need support related to:                  ⌚️Assignment / አሳይመንት                  ⌚️Research / ሪሰርች                  ⌚️Proposal / ፕሮፖዛል                  ⌚️Term Paper /  ተረም ፔፐር                  ⌚️Case study/ ኬዝ ስተዲ                  ⌚️Article Review                  ⌚️Mini research                  ⌚️Business plan 🥭Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏

Multiple regression assumptions diagnostic How can we test regression assumptions? Before performing regression analysis, it is important to check regression assumptions like multicollinerity, normality, linearity, autocorrelation, and homoskasticity. 1. Normally distribution It refers to the normal distribution of residuals or error terms. It can be tested using either graphical methods by histogram, predicted probability (p-p) plots and plotted points or statistical methods by using kurtosis and skewness values. Statistical methods are better than graphical methods. A data is normally distributed when the skewness and kurtosis values are between -2 and 2. 2. Multicollinearity It refers to the relationship between independent variables or predictors. That is, predictors should not be highly correlated. It can be checked by using either correlation coefficient between independent variables, using Tolerance and variance inflation factors (VIF) values of variables or eigenvalue values. That is, there is no multicollinearity among independent variables (multicollinearity assumption is not violated) if the correlation coefficient is less than 0.8 or tolerance is above 0.1 and VIF is below 5. 3. Linearity It refers to the linear relationship between independent and outcome variables. In a scatter plot, linearity can be checked if points conform to a diagonal fitted line. If the assumption of multicollinearity and normality are not violated, don't worry about linearity. 4. Homoscedasticity Homoscedasticity refers to the constant variance of error terms. Its opposite is heteroscedasticity. If it fails (heteroscedastic), additional predictors are required to explain results, or transform the data using logarithm, square root, .... Homoscedastic if residuals evenly distributed between -2 and 2. 5. Autocorrelation It refers to independence of observation. The independent variable is said to be autocorrelated when the current value of Y is dependent on its previous value. It can be checked by using Durbin-Watson test (DW). If DW = 2 or approaches to 2, there is no autocorrelation. እናማክራለን For college and University students (Degree, Masters and PhD programs), if you need support on academic tasks such as: # article reviews # term papers #  Case studies # project work Research related tasks # title selection # title description and concept note # research  proposal # Research for graduation # Mini research # business  plan # project  proposals # Data analysis (SPSS, STATA, ...) and others, Training on basic statistical software's     🗝GIS                🗝  STATA                                  🗝 SPSS                                                🗝 R                                                         🗝 XLSTAT 🪜And other related software's...... also  any other Questions please contact us via ☎️ +251912688642 +251912688642 Telegram Account https://t.me/Research100stock https://t.me/Research100stock 👇👇👇👇👇👇👇👇 join and learn more https://t.me/star_research_consultancy 🙏🙏🙏🙏🙏🙏🙏🙏🙏