Unit: Inference for Categorical Data: Proportions Chapter: Type 1 & Type 2 Errors Reference: – Error, false Positive, Probability of making error, Critical Value, Rejection region, False Negative, Factors affecting Type 1 & type 2 errors, Z- tests & t- tests, One tailed & two tailed tests. After studying this chapter, you should be able […]
Unit: Inference for Categorical Data: Proportions Chapter: Type 1 & Type 2 Errors Reference: – Error, false Positive, Probability of making error, Critical Value, Rejection region, False Negative, Factors affecting Type 1 & type 2 errors, Z- tests & t- tests, One tailed & two tailed tests. After studying this chapter, you should be able […]
Unit: Inference for Categorical Data: Proportions Chapter: Interpreting P-Values & Population Proportion Reference: – Hypothesis Testing, Interpreting P values, Strength of Evidence, Confidence Intervals, comparing two population proportion, Chi- square test proportion, Interpreting Practical Significance, Application & Case studies. After studying this chapter, you should be able to: Hypothesis Testing & Interpreting P Values. Confidence […]
Unit: Inference for Categorical Data: Proportions Chapter: Constructing & Interpreting Reference: – Measures of center & Spread, Graphical displays, Measuring Associations, Residual Analysis, Probability Distributions, Sampling & Experimental Design, Inference for Categorical data, Interpreting Confidence Intervals, Bias & confounding, Simulation & Randomization, Sample Surveys. After studying this chapter, you should be able to: Measures of […]
Unit: Inference for Categorical Data: Chi Square Chapter: Appropriate Inference Procedure Reference: – Exploring data, Sampling & Experimental design, Probability, Inference, Confidence Intervals, Power & Sample size, Designing Studies, bivariate data, Probability models, Chi- square tests, Inference for categorical data, Inference for Means & Proportions, Multivariate data analysis. After studying this chapter, you should be able […]
Unit: Inference for Categorical Data: Chi Square Chapter: Chi – Square Test for Independence Reference: – Exploring data, Sampling & Experimental design, Probability, Inference, Confidence Intervals, Power & Sample size, Designing Studies, bivariate data, Probability models, Chi- square tests, Inference for categorical data, Inference for Means & Proportions, Multivariate data analysis. After studying this chapter, […]
Unit: Inference for Categorical Data: Chi – Square Chapter: Chi- square Test for Homogeneity Reference: – Categorical Data, Contingency tables, Expected & Observed frequencies, Hypothesis testing, Degree of freedom, Chi- square Statistics, Critical value, P value, Assumptions & Interpretations, Applications. After studying this chapter, you should be able to: Categorical Data & Contingency Tables. Hypothesis […]
Unit: Inference for Categorical Data: Chi – Square Chapter: Chi- square Test for Goodness of Fit Reference: – Categorical data, expected frequencies, Null & Alternative Hypothesis, Chi – square test statistics, Degrees of freedom, Critical value & P value, Decision rule, Interpretation, Assumptions & Conditions, Practical Application. After studying this chapter, you should be able […]
Unit: Inference for Quantitative Data: Means Chapter: Confidence intervals & Tests Reference: – Confidence intervals for means, Confidence intervals for proportions, Confidence intervals for Differences, Confidence intervals for Regressions, Hypothesis testing, comparing means, Comparing Proportions, Power and Sample Size. After studying this chapter, you should be able to: Confidence intervals for means. Confidence intervals for […]
Unit: Inference of Quantitative Data: Means Chapter: Interpreting P-values & Population means Reference: – p-value definition, small p-values, Statical significance, population mean interpretation, Directionality, Comparison to significance level, type 1 error, Practical significance, Interpreting p-values in context. After studying this chapter, you should be able to: P-value definition Statical significance. Directionality. Population mean interpretation. P-value […]