https://docs.google.com/spreadsheets/d/1JsbmShmOZUJlMTOmQccPU5_JckC5QpoVyYjXwJQ_
ID: 3301542 • Letter: H
Question
https://docs.google.com/spreadsheets/d/1JsbmShmOZUJlMTOmQccPU5_JckC5QpoVyYjXwJQ_OSs/edit?usp=sharing.
THE INFORMATION IS HERE_____PLEASE PLEASE Please copy and paste the above link
YOU NEED TO WORK OUT THE DATA PROBLEMs /statistical analysis using these 4 methods:
1) Wilson method to calculate 95% CIs around these estimates
2)the Chi-Square test for categorical variables,
3) t test for continuous variables,
4)ANOVA for multiple groups, regression analysis
thank you! it won't let me upload image...too large of a table i think but please copy and paste link excel sheet will open up
Explanation / Answer
Since the TB and ART results are available on the sample size and we know the population TB size, we fill first build a linear regression model on the ART as a function of TB. Then, using this fitted regression model, we make prediction for the number of ART's on the population TB count. Data is missing on 36 countries and they will be deleted. The three columns from your shared spreadsheet copied are columns C, D, and G. The R program below gives the desired result:
> tt <- read.csv("clipboard",header = TRUE,sep=" ")
> tt2 <- na.omit(tt)
> names(tt2)
[1] "TB" "ART" "TB_Pop"
> View(tt2)
> artlm <- lm(ART~TB,data=tt2)
> summary(artlm)
Call:
lm(formula = ART ~ TB, data = tt2)
Residuals:
Min 1Q Median 3Q Max
-55.144 -12.257 5.359 18.725 24.433
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.50959 5.49283 13.747 <2e-16 ***
TB 0.05766 0.06769 0.852 0.396
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 20.98 on 121 degrees of freedom
Multiple R-squared: 0.005961, Adjusted R-squared: -0.002254
F-statistic: 0.7256 on 1 and 121 DF, p-value: 0.396
> predict(artlm,newdata=data.frame(TB=tt2$TB_Pop))
1 2 3 4 5 6
3592.69537 107.22192 75.79788 75.68257 75.91320 709.75621
7 8 9 10 11 12
144.70013 156.23189 112.98780 467.58932 79.60336 20947.98912
13 14 15 16 17 18
75.50959 375.33526 138.93425 80.69888 456.05756 75.50959
19 20 21 22 23 24
144.70013 825.07377 536.77986 75.50959 89.34770 173.52952
25 26 27 28 29 30
882.73255 117.02391 2900.78997 179.29540 1171.02647 1286.34404
31 32 33 34 35 36
248.48594 53006.27259 375.33526 100.30287 940.39134 91.07746
37 38 39 40 41 42
1113.36769 75.62491 106.06874 2151.22579 107.79851 121.63662
43 44 45 46 47 48
75.85554 79.66102 107.22192 8205.39802 14490.20540 95.11358
49 50 51 52 53 54
271.54945 75.97086 438.75992 825.07377 161.99776 11088.33719
55 56 57 58 59 60
101.45604 93.38381 381.10114 78.56551 277.31533 306.14472
61 62 63 64 65 66
450.29168 2612.49605 75.85554 80.52590 317.67648 473.35519
67 68 69 70 71 72
116.44733 1286.34404 277.31533 128.55567 163826.45395 58887.46848
73 74 75 76 77 78
825.07377 998.05012 95.11358 93.96040 277.31533 1286.34404
79 80 81 82 83 84
106.06874 998.05012 6244.99939 111.25804 125.09614 767.41499
85 86 87 88 89 90
121.63662 118.75368 1055.70890 882.73255 77.46999 3362.06023
91 92 93 94 95 96
1978.24944 1632.29674 86.46476 77.64296 323.44236 91.65405
97 98 99 100 101 102
83.00523 83.00523 75.50959 2208.88457 8954.96221 11434.28988
103 104 105 106 107 108
767.41499 76.20150 2612.49605 132.01520 95.11358 1171.02647
109 110 111 112 113 114
33863.55656 77.35467 94.53699 97.41993 76.43213 190.82716
115 116 117 118 119 120
1978.24944 2208.88457 490.65283 213.89067 119.33027 2381.86092
121 122 123
432.99405 998.05012 6706.26966
>
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