Questions: 1. Assumptions of Parametric Tests -Know how to examine normality graphically (histogram, stem-and-leaf plot, normality plots) -Know how to examine normality statistically (Kolmogorov-Smirnov, Shapiro-Wilk tests) -Know how to examine homoscedasticity statistically (Bartlett's test, Levene's test) -Know the various data transformations, and what types of data they may be best used for -Know how to determine if a value is an outlier -Know the different options for dealing with outliers in your data

Solution Steps

1. To examine normality graphically, we can use histograms, stem-and-leaf plots, and normality plots (like Q-Q plots).
2. To examine normality statistically, we can use tests like the Kolmogorov-Smirnov test and the Shapiro-Wilk test.
3. To examine homoscedasticity statistically, we can use Bartlett's test and Levene's test.

To examine normality graphically, we can use the following methods:

• Histogram: A histogram displays the frequency distribution of a dataset. If the data is normally distributed, the histogram should resemble a bell curve.
• Stem-and-Leaf Plot: This plot shows the distribution of data while retaining the original data values. A symmetric stem-and-leaf plot indicates normality.
• Normality Plots (Q-Q Plot): A Q-Q plot compares the quantiles of the data to the quantiles of a normal distribution. If the data is normally distributed, the points should lie approximately on a straight line.

To examine normality statistically, we can use the following tests:

• Kolmogorov-Smirnov Test: This test compares the sample distribution with a reference probability distribution (e.g., normal distribution). The null hypothesis is that the sample comes from the reference distribution.
• Shapiro-Wilk Test: This test assesses the null hypothesis that a sample comes from a normally distributed population. A p-value greater than 0.05 indicates that the data is normally distributed.

To examine homoscedasticity (equal variances) statistically, we can use the following tests:

• Bartlett's Test: This test checks if multiple samples have equal variances. It is sensitive to departures from normality.
• Levene's Test: This test assesses the equality of variances for a variable calculated for two or more groups. It is less sensitive to non-normality compared to Bartlett's test.

Final Answer