Course Details

Subject {L-T-P / C} : FP6172 : Advanced Experimental Design and Statistical Methods Laboratory { 0-0-3 / 2}

Subject Nature : Practical

Coordinator : Dr. Sushil Kumar Singh

Syllabus

List of Experiments:
1. Comparison of two means: Conduct an experiment to compare means of the responses of two groups of participants which are randomly assigned to two different treatments. The experiment tests the hypothesis that the mean response in the two groups is different.

2. Analysis of Variance (ANOVA): Conduct an experiment to compare means of more than two groups using ANOVA. The hypothesis tested is whether the means of the groups are equal.

3. Factorial Design: Design and conduct an experiment with multiple independent variables to determine the effects of each variable and their interactions on a dependent variable. This experiment looks at the effects of two or more independent variables on a response variable. The hypothesis tested is whether the effect of each independent variable on the response variable is the same for all levels of the other independent variables.

4. Blocking and Randomization: Design and conduct an experiment to control for extraneous variables by blocking and randomizing the treatment assignments to reduce the influence of confounding variables.

5. Mixed-Design Analysis of Variance (ANOVA): Design and conduct an experiment with a combination of between-subjects and within-subjects factors to analyze the effects of each factor and their interactions. This experiment is a combination of between-subjects and within-subjects designs.

6. Multivariate Analysis of Variance (MANOVA): Design and conduct an experiment with multiple dependent variables to determine the effects of independent variables on a set of dependent variables. This experiment extends ANOVA to include multiple response variables. The hypothesis tested is whether the means of the response variables are equal across the groups.

7. Regression Analysis: Design and conduct an experiment to investigates the relationship between two variables. The hypothesis tested is whether there is a significant relationship between the variables.

8. Multiple Regression Analysis: Design and conduct an experiment to analyze the relationship between multiple independent variables and a single dependent variable. It is useful for determining which independent variables have the strongest impact on the response variable.

9. Logistic Regression: Design and conduct an experiment to analyze the relationship between a binary dependent variable and one or more independent variables. This experiment is used to model the probability of a binary response (e.g. success/failure). The hypothesis tested is whether the predictor variables are significant in explaining the variation in the binary response.

10. Generalized Linear Models (GLMs): Design and conduct an experiment to analyze the relationship between a dependent variable and one or more independent variables when the dependent variable is not normally distributed.

Course Objectives

Course Outcomes

On Completion of the lab course student will be able to: <br />1. Learn about the steps involved in the scientific method, including formulating a research question, designing an experiment, collecting data, and analyzing results. <br /> <br />2. Learn about different experimental designs, including between-subjects designs, within-subjects designs, and factorial designs. They will also learn about the importance of controlling for extraneous variables <br /> <br />3. Learn about various statistical tests, such as t-tests, ANOVA, regression analysis, and will be able to choose the appropriate test for a given research question and design. <br /> <br />4. Learn about key statistical concepts, such as hypothesis testing, p-values, effect sizes, and confidence intervals. They will also learn how to interpret and report the results of statistical tests. <br /> <br />5. Gain hands-on experience using statistical software, such as or SPSS, Minitab and Design Expert to conduct statistical analyses and generate graphs and tables to visually represent the results.

Essential Reading

Supplementary Reading