Statistics_India📊

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SDG India index 2023-24 ✅First launched in December 2018, the index has become the primary tool for monitoring progress on the SDGs in India. ✅It has also fostered competition among the states and UTs by ranking them on the global goals. ✅The index is developed in collaboration with the United Nations in India. ✅It tracks the progress of all states and UTs on 113 indicators aligned with the National Indicator Framework (NIF) of the Ministry of Statistics and Programme Implementation. How are states and UTs ranked? ✅ The SDG India Index scores range between 0–100, higher the score of a State/UT, the greater the distance to target achieved. ✅States and UTs are classified into four categories based on Index score: 🔸Aspirant: 0–49. 🔸Performer: 50–64. 🔸Front-runner: 65–99. 🔸Achiever: 100. Currently, there are no states in the aspirant and achiever category. 📍Key highlights ✅The composite score for India improved from 57 in 2018 to 66 in 2020-21 to further to 71 in 2023-24. ✅India has taken significant strides in accelerating progress on the SDGs between the 2020-21 and 2023-24 editions of the Index. ✅Noteworthy advancements have been observed in Goals 1 (No Poverty), 8 (Decent Work and Economic Growth), 13 (Climate Action). ✅The SDG India Index 2023-24 reports a positive trend in the performance of States and UTs in their SDG journey. ✅The scores for States now range from 57 to 79, while UTs score between 65 and 77. ✅This represents an improvement over the 2020-21 scores, where the range was 52 to 75 for States and 62 to 79 for UTs. ✅Uttarakhand and Kerala have taken the top spot among states with a score of 79 each while Bihar was ranked last with a score of 57.

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Choosing the right statistical test depends on the research question, data type, and distribution. Here's a general guide: 1. *Define the research question*: Clarify the problem or hypothesis to determine the type of test needed. 2. *Identify the data type*: - *Categorical* (nominal or ordinal): Chi-squared, Fisher's exact, or McNemar's tests. - *Continuous* (interval or ratio): T-tests, ANOVA, regression, or non-parametric tests. 3. *Check data distribution*: - *Normal distribution*: Parametric tests (e.g., t-tests, ANOVA). - *Non-normal distribution*: Non-parametric tests (e.g., Wilcoxon, Kruskal-Wallis). 4. *Consider the research design*: - *Comparing groups*: T-tests, ANOVA, or non-parametric equivalents. - *Relationships between variables*: Regression, correlation, or non-parametric equivalents. 5. *Sample size and power*: - *Small samples*: Non-parametric tests or exact tests. - *Large samples*: Parametric tests or asymptotic tests. 6. *Data meeting assumptions*: Check for normality, homogeneity of variance, and independence. 7. *Choose a test*: - *T-tests*: Compare means between two groups. - *ANOVA*: Compare means across three or more groups. - *Regression*: Model relationships between continuous variables. - *Non-parametric tests*: Use when data doesn't meet parametric assumptions. Some popular statistical tests include: - *T-tests*: Independent samples t-test, paired samples t-test. - *ANOVA*: One-way ANOVA, two-way ANOVA. - *Regression*: Simple linear regression, multiple linear regression. - *Non-parametric tests*: Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation. - *Chi-squared tests*: Goodness-of-fit, independence, or homogeneity tests. Remember to always check the test assumptions and interpret results in the context of your research question.

ANOVA (Analysis of Variance) and ANCOVA (Analysis of Covariance) are both statistical techniques used to analyze the difference between means of two or more groups. The key difference between them is that ANCOVA takes into account additional variables that may affect the outcome, whereas ANOVA does not. ANOVA: - Analyzes the difference between means of two or more groups - Assumes that the groups have equal variances - Does not control for any other variables that may affect the outcome ANCOVA: - Analyzes the difference between means of two or more groups while controlling for one or more covariates (additional variables) - Adjusts the means of the groups to account for the effects of the covariates - Provides a more accurate estimate of the treatment effect by removing the variance attributed to the covariates In summary, ANCOVA is an extension of ANOVA that allows researchers to control for additional variables that may influence the outcome, providing a more precise estimate of the treatment effect. Here's an example to illustrate the difference: Suppose we want to compare the average scores of three different teaching methods (A, B, and C) on a math test. ANOVA would analyze the difference between the mean scores of the three groups without considering any other factors. ANCOVA, on the other hand, could analyze the difference between the mean scores while controlling for variables like prior math knowledge, age, or gender. This would provide a more accurate estimate of the effect of each teaching method on the math test scores, while accounting for any differences in these additional variables. #statistics #correlation #anova #ancova @statistics_india

There are several types of regression analysis, including: 1. *Simple Linear Regression*: examines the relationship between a single independent variable and a dependent variable. 2. *Multiple Linear Regression*: explores the relationship between multiple independent variables and a dependent variable. 3. *Logistic Regression*: used for binary outcomes, where the dependent variable is a binary/categorical variable. 4. *Ridge Regression*: a type of regularized regression that helps to reduce model complexity and prevent overfitting. 5. *Lasso Regression*: another type of regularized regression that uses L1 regularization to reduce model complexity. 6. *Elastic Net Regression*: a combination of Ridge and Lasso regression. 7. *Non-Linear Regression*: used when the relationship between the independent and dependent variables is non-linear. 8. *Quantile Regression*: focuses on estimating either the median or other quantiles of the dependent variable. 9. *Time Series Regression*: examines the relationship between a dependent variable and one or more independent variables over time. 10. *Generalized Linear Regression*: extends traditional linear regression to accommodate non-normal distributions. 11. *Generalized Additive Models*: combines elements of regression and machine learning to model complex relationships. 12. *Robust Regression*: used when the data contains outliers or non-normality. 13. *Principal Component Regression*: uses principal component analysis to reduce the dimensionality of the data. 14. *Partial Least Squares Regression*: combines principal component analysis with regression to model complex relationships. These types of regression analysis can be used in various fields, including economics, finance, biology, and social sciences, to model and analyze the relationships between variables.

Year on Year Inflation Rate (%) based on Consumer Food Price Index (CPI) Press Release link of CPI for the month of June 2024 :- https://www.mospi.gov.in/sites/default/files/press_release/CPI_PR_12july24.pdf #KnowYourStats #DataForDevelopment #CPI #Retailinflation