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2-2 - Chapter 2 Summarizing Data What happens if we change...

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Unformatted text preview: Chapter 2 - Summarizing Data What happens if we change some of the values in our list? How will the change affect the mean? The median? The standard deviation? Example 14: Changing Units Find the mean, median, and standard deviation for each of the following lists. [/27 M=7' er: [HM b) 15,19,18,16,17 (+10) M1! M=l7 6": [J—HL“ c)10,18,16,12,14 (x Z) M: l‘l M=lLl _ 6‘: 2. 87.3 d) -15,-27,-24,-18,-21 (x ”53> M: —Zl M = ’2] CT: H.243 e) 13,21,19,15,17» (x1 3+3) ' #2 l7 M: l7 0’: 2.828 What rules can we deduce from this example? . If adding (subtracting) the same number to each value in a data set: M J(Cd/3?: add/Sub‘éf‘acf same number CS‘/‘S 1 (lo Chan ¢ . If multiplying each value by positive constant: , 61/] z _ mq/il‘P/y by 54m: Poshfl'i/c Cori sigma!- o If multiplying each value by a negative constant: \ M‘ M/X‘ : MM prlx/ b\/ 54m: (127151 Ve consian‘é c—/.5 : multiply by 4105. Value 01: 90451994 f . If adding and multiplying, a shift and scale change: ’ A MVM/Y : all changes cagapflyp .. 5/3 : on\\/ .q-P-figc'l-eof by mH/l—Ll‘p ’I‘Ccfigl‘o/I El" 4b behalf: \/a /qe 5 , 30 @[email protected]?$13337.35303311333!!!iiiiiiifliiial Chapter 2 — Summarizing Data I 2.4 The Normal Approximation for Data What if we want to look at intervals besides the one given in the Empirical Rule? We can use standardized scores or z-scores. Z—scores tell how many SDs a data value is above or below the sample mean data value. raw score — mean z-score = ————_——_— standard devnatlon Example 15: ACT vs SAT Abigail and Emily wanted to compare their mathematics standardized test scores, but Abigail only took the ACT, she got a 25 on the math section, and Emily only took the SAT, she got a 620 on the math section. Nationwide the math ACT scores followed a normal curve with mean 21 and standard deviation 6 while the math SAT scores followed a normal curve with mean 500 and standard deviation .100. Using this information, how do Abigail and Emily’s scores compare? ‘ ‘ ZB'VZ\ __ Abifiou I: Z '5— T = :2; = O .(o . ‘ (922)- Bob _ (10 ,_ ’ Eml\\/: Z: '00 _ ,Iéo .- ['2‘ EMMY GLOQ ‘better 1' Once we calculate the z-scores, we can find the area over z—score-intervals using the standard normal curve. Example 16: Areas Use the standard normal curve, Table E, to find the following areas. a) The area less than 2. AM ls“ <5“ 2-00 => 0.61772. b) TWEN‘j‘éNn-Handz. Alba (£10501: 2.00 - A(‘€4\ [e95 c‘F’Z.O<) cme area gfeatoer than 1.5. O' 0‘77"}- - 0‘071'8 :- l'Areq “la-P4; 5? l- ‘3 s l" al.4352-—o ocp . —- . 4, Area le-fibg-F ~i.=‘s :- n d) The area between 1.25 and 1.75. Afeq le-p‘l: OF [.713 , Area. Kept o'F‘ 1.25 31 Chapter 2 — Summarizing Data 1 | Normal Approximation for Data .1 In summary, we can estimate the proportion of the data that are in an interval of {1 interest in three steps: 1 Step 1. Draw a number line and shade the interval of interest. Step 2. Convert the interval endpoints into z—scores using the formula ' raw score — mean z-score 2 —-—~—_———. standard deVIatIon Step 3. Find the area under the standard normal curve over the z-score interval. Example 17: ACT-vs SAT, revisited Using the information given in Example 15, answer the following questions. a) What percent of people did worse than Abigail on the math ACT? 2; ZEJO’Zl : (9-2: Area lane-é o‘F O.(o7 : (9.7ng _ b) What percent of people did better than Emily on the math SAT? __ (920—500 __ '2' too ’ Ll Area kept mt _\.’L“- own-M ' f;, c) What was Emily’s percentile on the math SAT? Area leg-E o’F L1 1 Mat—lei - 88.5441 (if-70’”; d) What score on the SAT corresponds to the 85 percentile? Afaot left o‘F 7: -— 0.9500 ”2"; L036»? X7520 :1.03(o7 :1) ix: (003.(o7)x éOL.’ e) What score on the ACT corresponds to the 20th percentile? Area le-Qt o“F 52-" o. 2000 14: ~ 0. 8H2. , 32 @ Chapter 2 — Summarizing Data Example 18: Heights of Men The heights of young men are approximately normally distributed with mean 70 inches and standard deviation 3.5 inches. a) What percent of men are shorter than 75 inches? ,_ “7%”70 E»- %.5_ ._ Lazar; Neat (arty? Wise :2: AW {mint 5%: we 2.— omega W b) What percent of men are taller than 60 inches? 3' ””5535, ‘1' -Z.“8‘37€ c) What percent of men are between 70 and 80 inches tall? ‘10 ~ ‘70 =- 70 w; .5517“ 3.15 :0 2%: $2.55 :2,@%.t Afea Ea'iwecfl %\ amfii T27; *- AFCa left a? 21 metres flail-g 65; g ‘ :21'6163761 ~0.5» :EO.“—lqw?fi\ 7 “lei/76367533} d) What percent of men are between 72 and 82 inches tall? ,- 72—70 fi ” 52~7© a; r 6.5 " p.57l‘i Z1’ 3%? :1? ”3.5-{28$ ‘Afaat heft/06894 %\ an 2'2. ‘ :: Afecl [291019 65: Z73,” A(‘eq gaff {3: El 1‘1 acacia??? 0.’71‘b‘7‘—‘ azgq :{ZQHWOE e) What height corresponds to the 70th percentile? Area (E‘p'é of Z 3 0-7030 av, oezH / X“70 20,524 >2> X1 71.93%” («3.5 ' _ f) What height corresponds to the 50th percentile? Afcta [94715 of 2.; 3"" C3 . 5&66 .5 Chapter 2 —- Summarizing Data Boxplot: The Five-Number Summary us quartiles. The quartiles Q1, Q2, and Q3 divide the data into four equal parts. 1. Sort the data from smallest to largest and compute the median. This is the second quartile, Q2. 2. The first quartile, Q, is the median of all the data to the left of Q2. 3. The third quartile, Q3, is the median of all the data to the right of Q2. Example 19: Exam scores Find Q1, Q2, and Q3 for the following set of Exam scores. To compare data, we can draw side- -b-y s-ide boxplots using five summary values. 1. 2. 3. 4. 5. These five values are referred to as the five-number summary and the corresponding boxplOt is called a five-number boxplot. If outliers are present, denote the outliers using asterisks, (*), and denote potential outliers with circles, (°). In tead of using the mean and the standard deviation to summarize the data, we can if at! l 84 88 68 68 95 81 79 81 79 82 .. 79 81 79 _ 9o 81 1' j e <5 68 7‘1 7 ‘1 mm CUTVODTMED’IAIO! the minimum value, the first quartile, the median, the third quartile, and the maximum value. 34 ...
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