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)
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