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10 KiB
10 KiB
Quantitative Methods
Title slide slide
(org-show-animate '("Quantitative Methods, Part-II" "Introduction to Statistical Inference" "Vikas Rawal" "Prachi Bansal" "" "" ""))
Sampling Distributions
Sampling Distributions slide
Sampling Distributions slide
- $Standard.error = \frac{\sigma}{\sqrt{mean}}$
Variable | Value |
---|---|
Standard deviation of population ($\sigma$) | 130 |
Standard errors of samples of size | |
5 | 58 |
20 | 29 |
50 | 18 |
200 | 9 |
Introduction to Hypothesis Testing
Transforming the Distribution to Standard Normal slide
Distribution of sample mean with unknown population variance slide
t-test
- One Sample t-test
- data: t9$wage
- t = 432.99, df = 37634, p-value < 0.00000000000000022
- alternative hypothesis: true mean is not equal to 0
- 95 percent confidence interval:
- 289.7136 292.3484
- sample estimates:
- mean of x
- 291.031
readRDS("plfsdata/plfsacjdata.rds")->worker
worker$standardwage->worker$wage
worker->t9
t.test(t9$wage)
subset(worker,sex!=3)->t9
factor(t9$sex)->t9$sex
t.test(wage~sex,data=t9)
Welch Two Sample t-test data: wage by sex t = 79.02, df = 13483, p-value < 0.00000000000000022 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 104.6563 109.9805 sample estimates: mean in group 1 mean in group 2 310.8974 203.5790
subset(worker,sex!=3)->t9
as.numeric(t9$gen_edu_level)->t9$gen_edu_level
factor(t9$sex)->t9$sex
t9[gen_edu_level>=8,.(schooled=length(fsu)),.(sex)]->a
t9[,.(all=length(fsu)),.(sex)]->b
prop.test(a$schooled,b$all)
2-sample test for equality of proportions with continuity correction data: a$schooled out of b$all X-squared = 847.73, df = 1, p-value < 0.00000000000000022 alternative hypothesis: two.sided 95 percent confidence interval: -0.1694726 -0.1525728 sample estimates: prop 1 prop 2 0.09245986 0.25348253