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

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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)

Sample Solution

To complete this task, I will assume that the yearly_sales.csv file has been loaded into R and is stored as a data frame called “sales_df”.

Descriptive statistics of sales_total:

phpCopy codesummary(sales_df$sales_total)
sd(sales_df$sales_total)
var(sales_df$sales_total)

The correlation of number_of_order to sales_total:

bashCopy codecor(sales_df$number_of_order, sales_df$sales_total)

Plot the scatter graph of number_of_order

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