Presentation is loading. Please wait.

Presentation is loading. Please wait.

Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means.

Similar presentations


Presentation on theme: "Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means."— Presentation transcript:

1 Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means

2 Ch 162 Statistics Versus Parameters Statistics: values that are computed from information provided by a sample Parameters: values that are computed from a complete census which are considered to be precise and valid measures of the population Parameters represent “what we wish to know” about a population. Statistics are used to estimate population parameters.

3 Ch 163

4 4 The Concepts of Inference and Statistical Inference Inference: drawing a conclusion based on some evidence Statistical inference: a set of procedures in which the sample size and sample statistics are used to make estimates of population parameters

5 Ch 165

6 6 How to Calculate Sample Error (Accuracy) spsp Where z = 1.96 (95%) or 2.58 (99%)

7 Ch 167 Accuracy Levels for Different Sample Sizes At 95% ( z = 1.96) n p=50%p=70%p=90% 10±31.0% ±28.4% ±18.6% 100 ±9.8% ±9.0% ±5.9% 250 ±6.2% ±5.7% ±3.7% 500 ±4.4% ±4.0% ±2.6% 1,000 ±3.1% ±2.8% ±1.9% The “p” you found in your sample 1.96 times s p 95% Confidence interval: p ± 1.96 times s p

8 Ch 168 Parameter Estimation Parameter estimation: the process of using sample information to compute an interval that describes the range of values of a parameter such as the population mean or population percentage is likely to take on

9 Ch 169 Parameter Estimation Parameter estimation involves three values: 1.Sample statistic (mean or percentage generated from sample data) 2.Standard error (variance divided by sample size; formula for standard error of the mean and another formula for standard error of the percentage) 3.Confidence interval (gives us a range within which a sample statistic will fall if we were to repeat the study many times over

10 Ch 1610 Parameter Estimation Statistics are generated from sample data and are used to estimate population parameters. The sample statistic may be either a percentage, i.e., 12% of the respondents stated they were “very likely” to patronize a new, upscale restaurant OR The sample statistic may be a mean, i.e., the average amount spent per month in restaurants is $185.00

11 Ch 1611 Parameter Estimation Standard error: while there are two formulas, one for a percentage and the other for a mean, both formulas have a measure of variability divided by sample size. Given the sample size, the more variability, the greater the standard error.

12 Ch 1612 Parameter Estimation The lower the standard error, the more precisely our sample statistic will represent the population parameter. Researchers have an opportunity for predetermining standard error when they calculate the sample size required to accurately estimate a parameter. Recall Chapter 13 on sample size.

13 Ch 1613 Standard Error of the Mean

14 Ch 1614 Standard Error of the Percentage

15 Ch 1615 Parameter Estimation Confidence intervals: the degree of accuracy desired by the researcher and stipulated as a level of confidence in the form of a percentage Most commonly used level of confidence: 95%; corresponding to 1.96 standard errors

16 Ch 1616 Parameter Estimation What does this mean? It means that we can say that if we did our study over 100 times, we can determine a range within which the sample statistic will fall 95 times out of 100 (95% level of confidence). This gives us confidence that the real population value falls within this range.

17 Ch 1617 Theoretical notion Take many, many, many samples Plot the p’s 95 % will fall in confidence interval (p ± z times s p) How do I interpret the confidence interval? 2.5% 95%

18 Ch 1618 Parameter Estimation Five steps involved in computing confidence intervals for a mean or percentage: 1.Determine the sample statistic 2.Determine the variability in the sample for that statistic

19 Ch 1619 Parameter Estimation 3.Identify the sample size 4.Decide on the level of confidence 5.Perform the computations to determine the upper and lower boundaries of the confidence interval range

20 Ch 1620 Parameter Estimation Using SPSS: Estimating a Percentage Run FREQUENCIES (on RADPROG) and you find that 41.3% listen to “Rock” music. So, set p=41.3 and then q=58.7, n=400, 95%=1.96, calculate S p. The answer is 36.5%-46.1% We are 95% confident that the true % of the population that listens to “Rock” falls between 36.5% and 46.1%. (See p. 464).

21 Ch 1621 How to Compute a Confidence Interval for a Percent Determine the confidence interval using Sample size (n) 95% level of confidence (z=1.96) P=?%; q=100%-?% Lower boundary Upper boundary

22 Ch 1622 Estimating a Population Percentage with SPSS How do we interpret the results? –Our best estimate of the population percentage that prefers “Rock” radio is 41.3 percent, and we are 95 percent confident that the true population value is between 36.5 and 46.1 percent.

23 Ch 1623 Parameter Estimation Using SPSS: Estimating a Mean SPSS will calculate a confidence interval around a mean sample statistic. From the Hobbit’s Choice data assume –We want to know how much those who stated “very likely” to patronize an upscale restaurant spend in restaurants per month. (See p. 465.)

24 Ch 1624 Parameter Estimation Using SPSS: Estimating a Mean We must first use DATA, SELECT CASES to select LIKELY=5. Then we run ANALYZE, COMPARE MEANS, ONE SAMPLE T-TEST. Note: You should only run this test when you have interval or ratio data.

25 Ch 1625

26 Ch 1626

27 Ch 1627 Parameter Estimation Using SPSS: Estimating a Percentage Estimating a Percentage: SPSS will not calculate for a percentage. You must run FREQUENCIES to get your sample statistic and n size. Then use the formula p±1.96 S p. AN EXAMPLE: We want to estimate the percentage of the population that listens to “Rock” radio.

28 Ch 1628 Estimating a Population Percentage with SPSS Suppose we wish to know how accurately the sample statistic estimates the percent listening to “Rock” music. –Our “best estimate” of the population percentage is 41.3% prefer “Rock” music stations (n=400). We run FREQUENCIES to learn this. –But how accurate is this estimate of the true population percentage preferring rock stations?

29 Ch 1629 Estimating a Population Mean with SPSS How do we interpret the results? – My best estimate is that those “very likely” to patronize an upscale restaurant in the future, presently spend $281 dollars per month in a restaurant. In addition, I am 95% confident that the true population value falls between $267 and $297 (95% confidence interval). Therefore, Jeff Dean can be 95% confident that the second criterion for the forecasting model “passes” the test.

30 Ch 1630 Hypothesis Testing Hypothesis: an expectation of what the population parameter value is Hypothesis testing: a statistical procedure used to “accept” or “reject” the hypothesis based on sample information Intuitive hypothesis testing: when someone uses something he or she has observed to see if it agrees with or refutes his or her belief about that topic

31 Ch 1631 Hypothesis Testing Statistical hypothesis testing: –Begin with a statement about what you believe exists in the population –Draw a random sample and determine the sample statistic –Compare the statistic to the hypothesized parameter

32 Ch 1632 Hypothesis Testing Statistical hypothesis testing: –Decide whether the sample supports the original hypothesis –If the sample does not support the hypothesis, revise the hypothesis to be consistent with the sample’s statistic

33 Ch 1633 What is a Statistical Hypothesis? A hypothesis is what someone expects (or hypothesizes) the population percent or the average to be. If your hypothesis is correct, it will fall in the confidence interval (known as supported). If your hypothesis is incorrect, it will fall outside the confidence interval (known as not supported)

34 Ch 1634 How a Hypothesis Test Works Sample Population Exact amount---- Uses sample error percent-----Test against Ho average-----Test against Ho Test hypothesis

35 Ch 1635 How to Test Statistical Hypothesis 2.5% 95%

36 Ch 1636 Testing a Hypothesis of a Mean Example in Text: Rex Reigen hypothesizes that college interns make $2,800 in commissions. A survey shows $2,750. Does the survey sample statistic support or fail to support Rex’s hypothesis? (p. 472)

37 Ch 1637 Since 1.43 z falls between -1.96z and z, we ACCEPT the hypothesis.

38 Ch 1638 How to Test Statistical Hypothesis 2.5% 95% Supported Not Supported

39 Ch 1639 The probability that our sample mean of $2,800 came from a distribution of means around a population parameter of $2,750 is 95%. Therefore, we accept Rex’s hypothesis.

40 Ch 1640 Hypothesis Testing Non-Directional hypotheses: hypotheses that do not indicate the direction (greater than or less than) of a hypothesized value

41 Ch 1641 Hypothesis Testing Directional hypotheses: hypotheses that indicate the direction in which you believe the population parameter falls relative to some target mean or percentage

42 Ch 1642 Using SPSS to Test Hypotheses About a Percentage SPSS cannot test hypotheses about percentages; you must use the formula. See p. 475

43 Ch 1643 Using SPSS to Test Hypotheses About a Mean In the Hobbit’s Choice Case we want to test that those stating “very likely” to patronize an upscale restaurant are willing to pay an average of $18 per entrée. DATA, SELECT CASES, Likely=5 ANALYZE, COMAPRE MEANS, ONE SAMPLE T TEST ENTER 18 AS TEST VALUE Note: z value is reported as t in output.

44 Ch 1644

45 Ch 1645

46 Ch 1646 What if We Used a Directional Hypothesis? Those stating “very likely” to patronize an upscale restaurant are willing to pay more than an average of $18 per entrée. Is the sign (- or +) in the hypothesized direction? For “more than” hypotheses it should be +; if not, reject.

47 Ch 1647 What if We Used a Directional Hypothesis? Since we are working with a direction, we are only concerned with one side of the normal distribution. Therefore, we need to adjust the critical values. We would accept this hypothesis if the z value computed is greater than (95%).

48 Ch 1648


Download ppt "Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means."

Similar presentations


Ads by Google