# Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.1 Chapter 14 Statistical Inference: Review of Chapters 12 & 13.

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Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.1 Chapter 14 Statistical Inference: Review of Chapters 12 & 13

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.2 Which technique to use?

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.3 Identifying the Correct Technique… The two most important factors in determining the correct statistical technique to use are:  the problem objective, (i.e. describe one population or compare two populations)  and the data type. (i.e. interval data or nominal data) Once these factors are determined, our analysis extends to other factors (e.g. type of descriptive measure [central location? variability?], etc.)

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.4 Figure 14.1… The flowchart in your textbook (Figure 14.1) describes the logical process that allows us to identify the appropriate method to use for the problem. Start at the top and work your way down the chart… The following slides are an interactive version of this flowchart…

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.5 Figure 14.1: Flowchart of Techniques… Problem objective? Describe a populationCompare two populations Click on the mouse icon to follow the branch of the flowchart to the next level… Skip flowchart, go to examples…

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.6 Figure 14.1: Flowchart of Techniques… Problem objective? Describe a population Data type? Interval Nominal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.7 Figure 14.1: Flowchart of Techniques… Problem objective? Describe a population Data type? Interval Type of descriptive measurement? Central location Variability

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.8 Slide 12.19 : Identifying Factors… Factors that identify the t-test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.9 Slide 12.29 : Identifying Factors… Factors that identify the chi-squared test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.10 Figure 14.1: Flowchart of Techniques… Problem objective? Describe a population Data type? Nominal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.11 Slide 12.42 : Identifying Factors… Factors that identify the z-test and interval estimator of p : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.12 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Nominal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.13 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Nominal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.14 Slide 13.64 : Identifying Factors… Factors that identify the z-test and estimator for p 1 – p 2 Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.15 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central location Variability

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.16 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Variability

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.17 Slide 13.49 : Identifying Factors… Factors that identify the F-test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.18 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central Location Independent samples Matched pairs Experimental design?

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.19 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central Location Matched pairs Experimental design?

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.20 Slide 13.40 : Identifying Factors… Factors that identify the t-test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.21 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central Location Independent samples Experimental design? Population variances? UnequalEqual

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.22 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central Location Independent samples Experimental design? Population variances? Equal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.23 Slide 13.30 : Identifying Factors… Factors that identify the equal-variances t-test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.24 Figure 14.1: Flowchart of Techniques… Problem objective? Compare two populations Data type? Interval Type of descriptive measurement? Central Location Independent samples Experimental design? Population variances? Unequal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.25 Slide 13.31 : Identifying Factors… Factors that identify the unequal-variances t-test and estimator of : Top of Flowchart

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.26 Example 14.1… Is anti-lock braking system (ABS) in cars really effective? We would expect if it were effective that:  The number of accidents would decrease, and  The cost of accident repairs would be less. DataData were collected on 500 cars with ABS and 500 cars without. The number of cars involved in accidents was recorded, as was the cost of repairs. What can we conclude?

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.27 Example 14.1 (a)… Is there sufficient evidence to infer that the accident rate is lower in ABS-equipped cars than in cars without ABS? (If ABS is effective, we would expect a lower accident rate in ABS-equipped cars.) Accident rate =number of cars in accidents total number of cars This is nominal (i.e. categorical) data; either a car had an accident or it didn’t. The accident rate is a proportion. We want to compare cars with ABS against cars without. IDENTIFY

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.28 Example 14.1 (a)… Identify the correct technique… IDENTIFY Problem objective? Describing a single population Compare two populations Data type? Interval Nominal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.29 Example 14.1 (a)… The correct technique: has been identified. The next step is to translate the rest of the problem into the symbols and language of statistics: p 1 = proportion of cars without ABS involved in an accident p 2 = proportion of cars with ABS involved in an accident We want to test if ABS is effective, that is, we want to research if: p 1 > p 2, that is if H 1 : ( p 1 – p 2 ) > 0 IDENTIFY

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.30 Example 14.1 (a)… Since H 1 : ( p 1 – p 2 ) > 0 we have our null hypothesis: H 0 : ( p 1 – p 2 ) = 0 Hence this is a Case 1 type problem. Upon calculating our sample proportions… …we can use Excel to complete our analysis… COMPUTE

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.31 Example 14.1 (a)… Noting that z =.4663 is not greater than z Critical = 1.6449 (or alternatively, by looking at the p-value of.3205), we cannot reject H 0 in favor of H 1, that is, there is not enough evidence to infer that ABS equipped cars have fewer accidents than non-ABS equipped cars… INTERPRET

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.32 Example 14.1 (b)… When accidents do occur, we would expect the severity of accidents to be lower in ABS-equipped cars (assuming that ABS is effective), thus we are interested in this question: Is there sufficient evidence to infer that the cost of repairing accident damage in ABS-equipped cars is less than that of cars without ABS? The cost of repairs is interval data. We need a measure to compare the two populations of cars in a meaningful way… IDENTIFY

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.33 Example 14.1 (b)… Identify the correct technique… IDENTIFY Problem objective? Describing a single population Compare two populations Data type? Interval Nominal Type of descriptive measurements? Central location Variability …continues…

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.34 Example 14.1 (b)… Identify the correct technique (continued)… IDENTIFY Equal?? Population variances equal? Independent samplesMatched pairs Unequal?? Experimental design? Central location e.g. we are not comparing a head-on collision of an ABS equipped car with a head-on collision of a non-ABS car… Which one is it?

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.35 Example 14.1 (b)… Identify the correct technique (continued)… IDENTIFY Equal?? Population variances equal? Unequal?? Apply the F-test of there is not enough evidence to infer that the variances differ…

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.36 Example 14.1 (b)… Identify the correct technique (continued)… …we have the right technique! Let’s proceed with our hypotheses set-up… IDENTIFY Equal Population variances equal? Unequal

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.37 Example 14.1 (b)… We want to research whether or not the mean cost of repairing cars without ABS brakes (population 1) is greater than the mean cost of repair of cars equipped with ABS brakes (population 2), i.e.: IDENTIFY

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.38 Example 14.1 (b)… Applying the Data Analysis tools in Excel to our data… Indeed, there is sufficient evidence to support the belief that non-ABS equipped cars do indeed have higher accident repair costs than ABS equipped cars. INTERPRET

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.39 Example 14.1 (c)… In part (b) we’ve shown that ABS-equipped cars suffer less damage in accidents (as measured by repair costs); can we estimate how much cheaper they are to repair on average compared to cars without ABS brakes. Our path through the flowchart is the same, that is, we are comparing the measure of central location of independent samples of interval data from two populations who’s variances are equal… IDENTIFY

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 14.40 Example 14.1 (c)… The estimator of interest is: Assuming a 95% confidence interval… …we estimate the cost of repair for a non-ABS equipped car of between \$71 and \$651 over an ABS equipped car. CALCULATE

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