Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series by Mario F. Triola

10-2 Definition A correlation exists between two variables when the values of one are somehow associated with the values of the other in some way. A linear correlation exists between two variables when there is a correlation and the plotted points of paired data result in a pattern that can be approximated by a straight line.

Notation for the Linear Correlation Coefficient

Formula The linear correlation coefficient r measures the strength of a linear relationship between the paired values in a sample. Here are two formulas: Technology can (and will be used to) compute this

Properties of the Linear Correlation Coefficient r 2. If all values of either variable are converted to a different scale, the value of r does not change. 3. The value of r is not affected by the choice of x and y. Interchange all x- and y-values and the value of r will not change. 4. r measures strength of a linear relationship. 5. r is very sensitive to outliers, which can dramatically affect the value of r.

Example The paired shoe / height data from five males are listed below. Use a computer or a calculator to find the value of the correlation coefficient r.

Example - Continued Requirement Check: The data are a simple random sample of quantitative data, the plotted points appear to roughly approximate a straight-line pattern, and there are no outliers.

Example - Continued A few technologies are displayed below, used to calculate the value of r.

Chapter 8 Main Objective The main objective of this chapter is to develop the ability to conduct hypothesis tests for claims made about a population proportion p, a population mean μ, or a population standard deviation σ. page 386 of Elementary Statistics, 10th Edition Various examples are provided below definition box

Key Concept This section presents individual components of a hypothesis test. We should know and understand the following: How to identify the null hypothesis and alternative hypothesis from a given claim, and how to express both in symbolic form How to calculate the value of the test statistic, given a claim and sample data How to choose the sampling distribution that is relevant How to identify the P-value or identify the critical value(s) How to state the conclusion about a claim in simple and nontechnical terms

Definitions A hypothesis is a claim or statement about a property of a population. A hypothesis test is a procedure for testing a claim about a property of a population.

Test Statistic The test statistic is a value used in making a decision about the null hypothesis, and is found by converting the sample statistic to a score with the assumption that the null hypothesis is true.

Step 5 Identify the Test Statistic and Determine its Sampling Distribution

Critical Region The critical region (or rejection region) is the set of all values of the test statistic that cause us to reject the null hypothesis. For example, see the red-shaded region in the previous figures.

Critical Value A critical value is any value that separates the critical region (where we reject the null hypothesis) from the values of the test statistic that do not lead to rejection of the null hypothesis. The critical values depend on the nature of the null hypothesis, the sampling distribution that applies, and the significance level α.

 α is divided equally between the two tails of the critical region Two-tailed Test  α is divided equally between the two tails of the critical region

Left-tailed Test  All α in the left tail

Right-tailed Test  All α in the right tail

Example For the XSORT birth hypothesis test, the critical value and critical region for an α = 0.05 test are shown below:

Type I and Type II Errors

8-4 Example Listed below are the measured radiation emissions (in W/kg) corresponding to a sample of cell phones. Use a 0.05 level of significance to test the claim that cell phones have a mean radiation level that is less than 1.00 W/kg. The summary statistics are: . 0.38 0.55 1.54 1.55 0.50 0.60 0.92 0.96 1.00 0.86 1.46

Example - Continued Step 1: The claim that cell phones have a mean radiation level less than 1.00 W/kg is expressed as μ < 1.00 W/kg. Step 2: The alternative to the original claim is μ ≥ 1.00 W/kg. Step 3: The hypotheses are written as:

Example - Continued Step 4: The stated level of significance is α = 0.05. Step 5: Because the claim is about a population mean μ, the statistic most relevant to this test is the sample mean: .

Example - Continued Step 6: Calculate the test statistic and then find the P-value or the critical value from Table A-3:

Example - Continued Step 7: Critical Value Method: Because the test statistic of t = –0.486 does not fall in the critical region bounded by the critical value of t = –1.812, fail to reject the null hypothesis.

Example - Continued Step 7: P-value method: Technology, such as a TI-83/84 Plus calculator can output the P-value of 0.3191. Since the P-value exceeds α = 0.05, we fail to reject the null hypothesis.

Example Step 8: Because we fail to reject the null hypothesis, we conclude that there is not sufficient evidence to support the claim that cell phones have a mean radiation level that is less than 1.00 W/kg.

Caution Never conclude a hypothesis test with a statement of “reject the null hypothesis” or “fail to reject the null hypothesis.” Always make sense of the conclusion with a statement that uses simple nontechnical wording that addresses the original claim.

Accept Versus Fail to Reject Some texts use “accept the null hypothesis.” We are not proving the null hypothesis. Fail to reject says more correctly that the available evidence is not strong enough to warrant rejection of the null hypothesis.