Question

Next, answer the following questions regarding the dataset you are analyzing (Exam Anxiety in Students)

• How did you treat missing or oddly coded data and outliers?
• What did you visually observe about your variables?
• What were the results of testing for normality for scale variables?
• What is the difference in the graphs for scale and non-scale variables?
• How would you summarize the descriptive statistics for each variable?
• How would you state a potential null and alternative hypotheses that might be of interest and importance to the test, based on your discoveries in this data?

Answer 1

How do you deal with missing values: Let’s look at some techniques to treat the missing values:

I. Deletion: Unless the nature of missing data is ‘Missing completely at random’, the best avoidable method in many cases is deletion.

II. Imputation:

a. Popular Averaging Techniques: Mean, median and mode are the most popular averaging techniques, which are used to infer missing values. Approaches ranging from global average for the variable to averages based on groups are usually considered.

b. Predictive Techniques: The imputation of missing values from predictive techniques assumes that the nature of such missing observations is not observed completely at random and the variables were chosen to impute such missing observations have some relationship with it, else it could yield imprecise estimates.

ways to deal with outliers in data:

1. Set up a filter in your testing tool

2. Remove or change outliers during the post-test analysis

3. Change the value of outliers

4. Consider the underlying distribution

5. Consider the value of mild outliers

Question: What did you visually observe your variables?

Answer: we observe the pattern in the variable, the distribution of the variable.

What were the results of testing for normality for scale variables?  

Answer: A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance). A number of statistical tests, such as the Student's t-test and the one-way and two-way ANOVA require a normally distributed sample population

What is the difference in the graphs for scale and non-scale variables?

Answer: Essentially, a scale variable is a measurement variable — a variable that has a numeric value. Variables with numeric responses are assigned the scale variable label by default. Nominal, ordinal and scale is a way to label data for a

non-scale variable: A variable measured on a "nominal" scale is a variable that does not really have any evaluative distinction. One value is really not any greater than another. A good example of a nominal variable is sex