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Unit 7, Inference for Quantitative Data(Mean) - Coggle Diagram
Unit 7, Inference for Quantitative Data(Mean)
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Significance Test
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T Test stats: 
Conditions for estimating population mean
- Random: Random Sampling
- Independence: Assume at least n*10
- Normality: n≥30(CLT), if CLT not met, draw a box plot(No severe skewness or outliers in the graph as the sample size is not big enough) - If conditions met, state, "All conditions met, use 1 sample T-test for population mean"
Conditions for estimating diff. in means
- Random: Random Sampling
- Independence: Assume at least n1x10 and n2x10
- Normality: n1≥30(CLT) and n2≥30, if CLT not met, draw a box plot for both population(No severe skewness or outliers in the graph as the sample size is not big enough)
- If conditions met, state, "All conditions met, construct 2 sample T-Test for difference in means"
State:
- P=Estimate the true mean...
- Ho: population mean=0
- Ha: population mean <,>, ≠
+ t cdf(LB:, UB:, df: )
For Ti-84, go to T-Test on the calculator
If P-value > significance level, We fail to reject Ho and there is no convincing evidence for [context]
If P-value < significance level, we reject Ho and there is convincing evidence for [context]
State:
- Pop. mean 1:
- Pop. mean 2:
- Ho: pop. mean 1= pop. mean 2
- Ha: pop. mean 1 <,>, ≠ pop. mean 2
For Ti 84, go to 2-Sample T-Test on the calculator