T-TestsSlide #1 1-sample t-test H o :  =  o (where  o = specific value) Statistic: Test Statistic: Assume: –  is UNknown – n is large (so that the.

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t-TestsSlide #1 1-sample t-test H o :  =  o (where  o = specific value) Statistic: Test Statistic: Assume: –  is UNknown – n is large (so that the test stat follows a t-distribution) n > 40, OR n > 15 and histogram is not strongly skewed, OR Histogram is approximately normal When: One population sampled, quantitative variable,  is UNknown. df = n-1

t-TestsSlide #2 A Full Example In Health magazine reported (March/April 1990) that the average saturated fat in one pound packages of butter was 66%. A food company wants to determine if its brand is significantly less than this overall mean. They analyzed a random sample of 96 one pound packages of its butter. Test the company’s hypothesis at the 1% level. Variable n Mean St. Dev. Min... %SatFat

Inference ConceptsSlide #3 Recipe for any Hypothesis Test 1) State the rejection criterion (  )  =0.01 2) State the null & alternative hypotheses, define the parameter(s) H o :  = 66 H a :  66 ….  is mean %SatFat for all 1-lb package of butter from this company 3) Determine which test to perform – Explain! 1-sample t-test … because (a) a single population (1-lb packages of butter from this company), (b) quantitative variable (%SatFat), and (c)  is unknown.

Inference ConceptsSlide #4 Recipe for any Hypothesis Test 4) Collect the data (address type of study and randomization) (i) Observational study (no control imparted on packages of butter) (ii) A random sample (n=96) was taken 5) Check all necessary assumption(s) (i)  is unknown (ii) n=96>40 6) Calculate the appropriate statistic(s)  x = 65.6 (in background)

Inference ConceptsSlide #5 Recipe for any Hypothesis Test 7) Calculate the appropriate test statistic df = 96-1 = 95 8) Calculate the p-value > ( distrib(-2.78,distrib="t",df=95) ) [1]

Inference ConceptsSlide #6 Recipe for any Hypothesis Test 9) State your rejection decision p-value (0.0033) <  (0.01) …. Reject H o 10) Summarize your findings in terms of the problem The mean percent saturated fat for all 1-lb packages of butter for this company appears to be less than that (=66) for the industry as a whole.

Inference ConceptsSlide #7 Recipe for any Hypothesis Test 11) If rejected H 0, compute a 100(1-  )% confidence region for parameter (i) 100(1-0.01)% = 99% (ii) Upper bound … because H a was less than (iii) t* = … from distrib(0.99,distrib=“t”,df=95,type=“q”) (iv) * (v) I am 99% confident that the mean percent saturated fat for all 1-lb packages of butter from this company is less than

t-TestsSlide #8 Practical Significance Is there a real difference between 66% and 65.6% saturated fat? If the sample size is large enough, any hypothesis can be rejected.