Consider this dataset of the spending habits of 100 families.Specifically, their food spending per month and their clothing spending per year....

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 below

3a) 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",100,89.29,254.59

R code Q3 = read.csv("A1spend.csv")

head(Q3)

# Find the correlation, and test it

cor(Q3$food, Q3$clothing)

cor.test(Q3$food, Q3$clothing)

# Make a scatterplot

plot(Q3$food, Q3$clothing)