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# Consider this dataset of the spending habits

Consider this dataset of the spending habits of 100 families. Specifically, their food spending per month and their clothing spending per year. (A1spend.csv)[8 points, 2 per question.] Use R code given below3a) Find the Pearson correlation coefficient. Test the hypothesis that the parameter rho = 0 at the 0.01 level?3b) How much of the variation in clothing spending can be explained by food spending?3c) Verify the statistical significance of the correlation with a two-sided t-test. Report the t-score, degrees of freedom and p-value.3d) Produce a scatterplot to better see the relation. Is there any trend in the plot that could be a problem?””,”family”,”food”,”clothing””1″,1,64.92,192.41″2″,2,54.8,157.11″3″,3,67.29,130.86″4″,4,40.93,86.04″5″,5,53.57,133.36″6″,6,67.98,172.41″7″,7,68.17,185.65″8″,8,59.89,75.71″9″,9,58.6,173.72″10″,10,106.49,271.21″11″,11,65.36,176.28″12″,12,46.85,101.28″13″,13,54.77,102.71″14″,14,106.36,144.17″15″,15,70.72,126.01″16″,16,72.83,167.29″17″,17,48.79,109.19″18″,18,108.42,161.13″19″,19,70.21,199.54″20″,20,78.05,131.06″21″,21,95.09,189.71″22″,22,110.69,284.47″23″,23,71.34,180.9″24″,24,69.65,157.82″25″,25,136.05,365.6″26″,26,32.68,82.36″27″,27,65.79,174.83″28″,28,36.58,83.17″29″,29,85.44,199.35″30″,30,93.34,139.55″31″,31,119.48,276.41″32″,32,84.42,264.79″33″,33,60.61,122.03″34″,34,139.73,321.28″35″,35,71.76,131.23″36″,36,153.36,350.51″37″,37,49.93,94.48″38″,38,80.19,170.11″39″,39,42.46,81.98″40″,40,108.91,265.58″41″,41,57.89,99.57″42″,42,144.11,216.34″43″,43,84.25,178.4″44″,44,65.96,62.81″45″,45,42.28,90.7″46″,46,53.37,104.04″47″,47,16.7,57.95″48″,48,125.85,59.94″49″,49,73.68,165.8″50″,50,54.45,160.27″51″,51,100.2,197.18″52″,52,76.39,95.65″53″,53,59.64,106.18″54″,54,39.86,72.23″55″,55,73.39,78.09″56″,56,80.46,201.05″57″,57,66.33,146.83″58″,58,86.94,196.89″59″,59,74.26,178.27″60″,60,91.54,16.31″61″,61,102.88,297.66″62″,62,85.2,106.87″63″,63,60.48,136.55″64″,64,59.06,123.26″65″,65,48.89,96.96″66″,66,106.24,265.09″67″,67,97.8,89.76″68″,68,119.06,250.68″69″,69,51.3,99.95″70″,70,103.76,182.52″71″,71,90.77,226.66″72″,72,79.14,142.14″73″,73,82.64,174.43″74″,74,110.68,271.6″75″,75,107.27,192.18″76″,76,84.43,207.31″77″,77,37.42,89.95″78″,78,75.12,119.19″79″,79,62.12,156.07″80″,80,100.18,319.53″81″,81,60.1,154.25″82″,82,42.48,99.23″83″,83,43.39,106.25″84″,84,91.76,79.14″85″,85,94.78,234.62″86″,86,34.29,86.45″87″,87,53.8,114.88″88″,88,124.46,199.54″89″,89,106.42,416.75″90″,90,77.54,205.8″91″,91,105.97,91.32″92″,92,49.36,137.42″93″,93,72.42,183.72″94″,94,82.29,161.55″95″,95,84.69,148.26″96″,96,62.6,102.64″97″,97,73.4,80.01″98″,98,102.76,248.9″99″,99,112.36,290.53″100″,100,89.29,254.59R code Q3 = read.csv(“A1spend.csv”)head(Q3)# Find the correlation, and test itcor(Q3\$food, Q3\$clothing)cor.test(Q3\$food, Q3\$clothing)# Make a scatterplotplot(Q3\$food, Q3\$clothing)

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