Statistics for Data Science and Business Analysis - Test for the Mean; Independent Samples (Part 2)

The video tutorial covers hypothesis testing with independent samples and unknown but equal variances. It revisits a dataset comparing apple prices in New York and LA, setting up null and alternative hypotheses. The tutorial explains using pooled variance and calculating the standard error, followed by determining the T-statistic and degrees of freedom. It interprets the results, highlighting the significance of the P-value, and concludes with a discussion on the implications of the findings and further questions for exploration.

What is the null hypothesis in the context of comparing apple prices in New York and LA?

• The mean price in New York is 20% higher than in LA.

• The mean price in New York is greater than in LA.

• The mean price in New York is less than in LA.

• The mean price in New York is equal to the mean price in LA.

Which formula is used to calculate the pooled variance when variances are unknown but assumed equal?

• The standard deviation formula

• The pooled variance formula

• The correlation coefficient formula

• The variance of means formula

How do you determine the degrees of freedom for the T statistic in this test?

• Combined sample size minus one

• Combined sample size minus the number of variables

• Sample size of LA minus sample size of New York

• Sample size of New York minus sample size of LA

What does a T score higher than four generally indicate?

• The test is extremely significant

• The test is inconclusive

• The test is moderately significant

• The test is not significant

What does a P value lower than 0.01 suggest about the test results?

• The test results are extremely significant

• The probability of a type 1 error is high

• The test results are not significant

• The null hypothesis is accepted