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S301 Week 10 Lecture & Textbook Notes

by: Lauren Detweiler

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S301 Week 10 Lecture & Textbook Notes STAT-S301

Marketplace > Indiana University > Statistics > STAT-S301 > S301 Week 10 Lecture Textbook Notes
Lauren Detweiler
IU
GPA 3.98
Hannah Bolte

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These notes cover the topics from Week 10 lectures, as well as all required concepts from the textbook that are not discussed in lecture.
COURSE
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Hannah Bolte
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Class Notes
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This 3 page Class Notes was uploaded by Lauren Detweiler on Sunday March 29, 2015. The Class Notes belongs to STAT-S301 at Indiana University taught by Hannah Bolte in Spring2015. Since its upload, it has received 87 views. For similar materials see Business Statistics in Statistics at Indiana University.

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Date Created: 03/29/15
S301 Week 10 Ch 162165 pgs 391405 Required concepts not covered in lecture I Hypothesis Testing a alevel probability of a Type I error b C the critical value for the sample mean X between the retain and reject regions c pvalues smallest alevel at which H0 can be rejected d Power the probability that a test can reject H0 11 Important Reminder a Signi cant does not mean important Required concepts covered in lecture 1 Testing H0 for Proportions vs Means Testing H0 with ztest Testing Hg with ttest Sample type proportion mean Hypotheses Ho 12 3 p0 Ho M S 0 Hazpgtp0 Hazpgtpo not covered in book 39 39 3 H 2 1 0 3 HT TCSt StatlSth Z m Z Tm tn1 m 533 2 P 1P SEQ 5quot se 2 Vs Compare to 20 ta1 a gt quot Note that although this table shows a set of hypotheses for a right tailed test S vs gt they could also be lefttailed 2 vs lt or two tailed vs at 11 t Test for the Mean and no a SE vs se i SE uses the population SD 0 and produces a z statistic ii se estimates the SE by using the sample SD in place of a and produces a t statistic b t statistic i The number of estimated standard errors from X to no deviation 0 f sample statistic from H0 X no t estimated standard error 0 f sample statistic sJ c t test d A test that uses a t statistic as the test statistic III Strategically Signi cant vs Economically Signi cant Critical Value for Test at alevel5 vs Observed Value from Sample I Using t stat 12499 178 gt 16455 100552499TINV0952499 REJECI Using pval pval1 TDISTI82499TRL E 00374 lt 005 oz REJECT I r 561 Usmg C C 1500 16455 m 151846 lt 1520 x REJECT C 4C C151846 1520 pvalue3 74 I C05 COC L L r 1 1 x 6 quott 4 3 471 19 1175 47953 14513 34 1495 74 u lt 163 5 1466 L2 lt3 ABC 73 45381 482 MIN 97 1437 66 1453 149343 1531 L2 1533 81 1536 51 153393 151189 194 SQ 1519 399 1 12quot 1879 36 1533 35 153C 7quot 1533 41 S E 3 1533 82 a Other things to consider i Sensitivity of rents to economic factors 1 Impact on rental agency s costs 2 Impact on renter s nancial stability ii Sensitivity to other external factors 1 Political 2 Environmental 3 Legal iii Opportunity Costs 1 Other more pro table expansions 2 Other more stable investments 3 Unforeseen opportunities b Statistically signi cant does not mean important i The size of the sample affects the pvalue of a test With enough data even a trivial difference from H0 leads to a statistically signi cant outcome ii Statistical signi cance does not mean that you have made an important or meaningful discovery iii Statistical signi cance should not be the only thing on which you base business decisions Use your brain intuition research consultants experts etc IV Con dence Intervals vs Hypothesis Testing a Statistically signi cant does not mean important i A con dence interval provides a range of parameter values that are compatible With the observed data ii iii iv A test provides a precise analysis of a speci c hypothesized value for a parameter Most people understand the implications of Cls more readily than tests 1 The difference is subtle For a twotailed test a CI will produce the same result if 1 oclevel Con dence Level For a onetailed test a CI will produce the same result if 1 oclevel Con dence Level assuming test statistic is not extreme in opposite direction of reject region 1 A CI may not produce the same result if 1 oclevel i Con dence Level

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