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Mean hypothesis testing with t-distribution
- Intro Lesson3:58
- Lesson: 116:46
- Lesson: 2a17:06
- Lesson: 2b18:24
- Lesson: 2c7:39
Mean hypothesis testing with t-distribution
Lessons
If σ is not known, then we cannot use the test statistic:
Z=nσx−μ
We will instead use the test-statistic:
Z=nsx−μ
So at the very least we must know the sample standard deviation, s.
Furthermore we will be using a t-distribution instead of our standard normal distribution to find our fail to reject region and our rejection region.
Z=nσx−μ
We will instead use the test-statistic:
Z=nsx−μ
So at the very least we must know the sample standard deviation, s.
- IntroductionHow do we test hypotheses about mean, when we don't know σ?
- 1.Hypothesis Testing Mean Claims without Knowing σ
A gravel company has been known in the past to overload their trucks. The load capacity is 2500lbs of gravel for one of their standard trucks. A total of 41 trucks were sampled and had an average load of 2550lbs, with a standard deviation of 150lbs. With a significance level of α=0.01 can it be said that this company overloads their trucks? - 2."Redline motorcycles" is a company that fixes and tunes motorcycles. A sample of 75 of their motorcycles had an average of 135hp, and a standard deviation of 35hp. Test the following claims with a 99% confidence level:a)"The average motorcycle produced by Redline has more than 125hp"b)"The average motorcycle produced by Redline doesn't have 125 hp"c)Compare the two answer found in the previous two parts
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10.
Hypothesis Testing
10.1
Null hypothesis and alternative hypothesis
10.2
Proving claims
10.3
Confidence levels, significance levels and critical values
10.4
Test statistics
10.5
Traditional hypothesis testing
10.6
P-value hypothesis testing
10.7
Mean hypothesis testing with t-distribution
10.8
Type 1 and type 2 errors
10.9
Chi-Squared hypothesis testing
10.10
Analysis of variance (ANOVA)
10.11
Chi-square goodness of fit test