Instructions Answer the questions below using Microsoft Excel, SAS or another statistical analysis program The data for questions 1 2 are located in an Excel spreadsheet on Canvas in the Exercises section You will be using the same dataset for questions 1 and 2 Data for question three is provided below Make sure you read through each question completely For the following questions, you will be using several methods to investigate the precision reliability of 50 individuals who completed replicate 24 hour recalls Specifically, you will be investigating the reproducibility of total fat intake (g) and Vitamin A intake (RAE) 1 (mcg) a Plot the fat intake from the first 24 hour recall on the x axis and the second 24 hour recall on the y axis (paste plot below) How well do the estimates of intake seem to correlate (comment without calculating a correlation coefficient) b Plot the Vitamin A intake from the first 24 hour recall on the x axis and the second 24 hour recall on the y axis (paste plot below) How well do the estimates of intake seem to correlate (comment without calculating a correlation coefficient) c Use Excel to calculate the Pearson correlation coefficient for dietary fat intake and Vitamin A intake Based on this information, which has better reliability and why do you think that is the case Pearson r Total Fat Intake (g) Vitamin A Intake (RAE) (mcg) 24 Hour Recall Day 1 24 Hour Recall Day 2 FFQ ID Total Fat (g) Vitamin A (RAE) (mcg) Total Fat (g) Vitamin A (RAE) (mcg) Total Fat (g) 21005 101 42 127 93 24 200 149 12 21006 43 245 39 7 248 13 7 21007 32 86 199 33 86 161 18 96 21008 107 95 455 74 51 816 229 14 21009 235 64 835 200 6 532 342 2 21010 60 87 423 51 13 394 70 1 21012 113 14 336 112 68 89 191 3 21013 103 38 38 21 79 22 209 6 21014 70 65 406 58 73 989 73 68 21015 45 75 650 89 17 913 11 5 21016 70 73 586 57 21 628 78 66 21017 70 08 99 5 74 32 151 86 34 21018 24 92 143 72 82 982 56 21019 23 33 56 22 01 131 1 43 21020 132 01 965 118 65 145 276 33 21022 47 23 349 33 9 440 6 77 21023 141 27 617 112 32 880 223 75 21024 43 87 553 49 76 2630 50 76 21025 38 28 61 74 78 938 9 48 21026 98 43 292 13 32 91 218 4 21027 114 42 859 96 79 446 189 47 21028 48 28 804 46 63 776 63 26 21029 115 8 777 186 13 1391 278 54 21031 42 25 459 36 8 576 56 23 21032 66 45 161 31 41 230 12 4 21033 53 74 203 49 09 588 34 69 21035 51 78 1328 25 3 851 39 12 21036 66 32 283 60 93 246 41 6 21037 75 87 706 82 63 636 48 65 21038 93 02 201 15 95 170 188 42 21039 46 02 1108 52 39 1382 97 43 21040 40 63 566 54 35 510 3 82 21041 70 4 544 41 64 214 36 6 21043 42 08 138 52 61 342 106 6 21045 49 17 1086 94 11 753 8 99 21046 49 54 441 93 19 857 11 6 21047 102 51 107 93 21 1423 171 4 21048 73 06 346 109 36 45 69 34 21049 82 87 437 77 59 421 52 23 21050 57 43 48 81 35 270 32 42 21051 134 11 808 104 68 308 263 4 21052 88 56 407 138 8 531 38 2 21054 18 37 47 42 5 947 3 68 21055 85 87 283 44 98 218 114 68 21057 115 96 460 187 68 811 197 56 21058 63 64 214 45 38 265 41 73 21059 22 97 203 88 58 874 99 4 21060 35 86 661 119 6 232 5 77 21061 64 06 658 74 84 177 40 84 21062 62 96 351 61 97 1370 45 5 1 RAE Retinol Activity Equivalents fPearson Correlation Objective indicate the extent to which two E ( X X) ( Y Y) variables are linearly related r E( X X) 2 ( Y Y) 2 Variable types Where, X mean of X variable Both are continuous variables Y mean of Y variable Joint distributions that is normally distributed x Independent variable Y Dependent variablePearson Correlation Positive Correlation o r 0 4 No correlation Negative Interpretation indicates a strong positive relationship Weak 0 00 t0 0 30 Moderate 0 31 to 0 65 Strong 0 66 to 1 00 1 indicates a strong negative relationship A result of zero indicates no relationship at all How to do it Excel SAS PEARSON(RANGE1, RANGE2) proc corr data All plots scatter (ELLIPSE NONE) var AGE COOK HOME PEARSON(B3 B52, D3 D52) run Pearson Correlation Coefficients 200 Prob Irl under HO Rho 0 180 Number of Observations 160 y 0 4671x 40 074 140 R2 0 198 COOK HOME PO COOK HOME 120 Y 100 COOK HOME PO 1 00000 0 41176 80 60 COOK HOME PO 0 0002

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Instructions : Answer the questions below using Microsoft Excel, SAS or another statistical analysis program.The data for questions 1 & 2 are located in an

Instructions: Answer the questions below using Microsoft Excel, SAS or another statistical analysis program.The data for questions 1 & 2 are located in an Excel spreadsheet on Canvas in the Exercises section. You will be using the same dataset for questions 1 and 2. Data for question three is provided below. Make sure you read through each question completely.

For the following questions, you will be using several methods to investigate the precision/ reliability of 50 individuals who completed replicate 24-hour recalls. Specifically, you will be investigating the reproducibility of total fat intake (g) and Vitamin A intake (RAE)[1] (mcg).

a. Plot the fat intake from the first 24-hour recall on the x-axis and the second 24-hour recall on the y-axis (paste plot below).How well do the estimates of intake seem to correlate (comment without calculating a correlation coefficient)?

b. Plot the Vitamin A intake from the first 24-hour recall on the x-axis and the second 24-hour recall on the y-axis (paste plot below).How well do the estimates of intake seem to correlate (comment without calculating a correlation coefficient)?

c. Use Excel to calculate the Pearson correlation coefficient for dietary fat intake and Vitamin A intake. Based on this information, which has better reliability and why do you think that is the case?

	Pearson r
Total Fat Intake (g)
Vitamin A Intake (RAE) (mcg)

	24 Hour Recall- Day 1	24 Hour Recall- Day 2	FFQ
ID	Total Fat (g)	Vitamin A (RAE) (mcg)	Total Fat (g)	Vitamin A (RAE) (mcg)	Total Fat (g)
21005	101.42	127	93.24	200	149.12
21006	43	245	39.7	248	13.7
21007	32.86	199	33.86	161	18.96
21008	107.95	455	74.51	816	229.14
21009	235.64	835	200.6	532	342.2
21010	60.87	423	51.13	394	70.1
21012	113.14	336	112.68	89	191.3
21013	103.38	38	21.79	22	209.6
21014	70.65	406	58.73	989	73.68
21015	45.75	650	89.17	913	11.5
21016	70.73	586	57.21	628	78.66
21017	70.08	99.5	74.32	151	86.34
21018	24.92	143	72.82	982	56
21019	23.33	56	22.01	131	1.43
21020	132.01	965	118.65	145	276.33
21022	47.23	349	33.9	440	6.77
21023	141.27	617	112.32	880	223.75
21024	43.87	553	49.76	2630	50.76
21025	38.28	61	74.78	938	9.48
21026	98.43	292	13.32	91	218.4
21027	114.42	859	96.79	446	189.47
21028	48.28	804	46.63	776	63.26
21029	115.8	777	186.13	1391	278.54
21031	42.25	459	36.8	576	56.23
21032	66.45	161	31.41	230	12.4
21033	53.74	203	49.09	588	34.69
21035	51.78	1328	25.3	851	39.12
21036	66.32	283	60.93	246	41.6
21037	75.87	706	82.63	636	48.65
21038	93.02	201	15.95	170	188.42
21039	46.02	1108	52.39	1382	97.43
21040	40.63	566	54.35	510	3.82
21041	70.4	544	41.64	214	36.6
21043	42.08	138	52.61	342	106.6
21045	49.17	1086	94.11	753	8.99
21046	49.54	441	93.19	857	11.6
21047	102.51	107	93.21	1423	171.4
21048	73.06	346	109.36	45	69.34
21049	82.87	437	77.59	421	52.23
21050	57.43	48	81.35	270	32.42
21051	134.11	808	104.68	308	263.4
21052	88.56	407	138.8	531	38.2
21054	18.37	47	42.5	947	3.68
21055	85.87	283	44.98	218	114.68
21057	115.96	460	187.68	811	197.56
21058	63.64	214	45.38	265	41.73
21059	22.97	203	88.58	874	99.4
21060	35.86	661	119.6	232	5.77
21061	64.06	658	74.84	177	40.84
21062	62.96	351	61.97	1370	45.5

[1] RAE = Retinol Activity Equivalents

\fPearson Correlation Objective: indicate the extent to which two E ( X - X) ( Y - Y) variables are linearly related r= - E( X - X) 2 ( Y - Y) 2 Variable types: Where, X- mean of X variable . Both are continuous variables Y = mean of Y variable . Joint distributions that is normally distributed x = Independent variable Y = Dependent variablePearson Correlation Positive Correlation o r=-0.4 No correlation Negative Interpretation: | indicates a strong positive relationship Weak: 0.00 t0 0.30 Moderate: 0.31 to 0.65 Strong: 0.66 to 1.00 -1 indicates a strong negative relationship A result of zero indicates no relationship at all How to do it? Excel SAS = PEARSON(RANGE1, RANGE2) proc corr data=All plots=scatter (ELLIPSE=NONE) ; var AGE COOK_HOME; =PEARSON(B3:B52, D3:D52) run; Pearson Correlation Coefficients 200 Prob > Irl under HO: Rho=0 180 Number of Observations 160 y = 0.4671x + 40.074 140 R2 = 0.198........" COOK HOME_PO COOK HOME 120 Y 100 COOK HOME PO 1.00000 0.41176 80 60 COOK_HOME_PO 0.0002