# Given the yearly sales in yearly_sales .csv file, complete the following: Show all the descriptive statistics of sales_total, including its standard deviation and variance.

Given the yearly sales in yearly_sales .csv file, complete the following: Show all the descriptive statistics of sales_total, including its standard deviation and variance. Correlation of number_of_order to sales_total. Plot the scatter graph of number_of_order to sales_total. Perform linear regression of number_of_order to sales_total. Draw the line of best fit (abline) over your graph. Perform T test as shown below and show your conclusion. Perform ANOVA test as shown below and show your conclusion. T test This is to test for the mean of one group; here we have sale_total. t.test(sales_total, mu = 249) # R command for t test H0: mu = 249 # null hypothesis H1: mu ≠ 249 # alternative hypothesis Rejection level = 0.05 (implies 95% confidence level) Do not Reject H0 if p-value is <= 0.05 Reject H0 if p-value is > 0.05 ANOVA test ANOVA is used to test the equality of mean for two groups; here we have Male and Female. anova(lm(data = myData, sales_total ~ factor(gender))) # R command for ANOVA test. H0: There is significant difference between Male and Female sales_total. H1: There is no significant difference between Male and Female sales_total. Rejection level = 0.05 (implies 95% confidence level) Do not Reject H0 if p-value is <= 0.05 Reject H0 if p-value is > 0.05

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