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# Data Description and Analysis with Spreadsheets DSCI 2710

UNT

GPA 3.53

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This 7 page Class Notes was uploaded by Dr. Lolita McCullough on Sunday October 25, 2015. The Class Notes belongs to DSCI 2710 at University of North Texas taught by Alan Kvanli in Fall. Since its upload, it has received 46 views. For similar materials see /class/229144/dsci-2710-university-of-north-texas in Decision Sciences at University of North Texas.

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Date Created: 10/25/15

123 6T1A 1 HT 1 1 1 Formula To find the percentile rank of a score x out of a set of n scores where x is included Emir 400 parser 3 Where B number of scores below x E number of scores equal to x n number of scores zxpa TRt 193635037t boxes useful for summarizing nominal or Ytbo39b1 YtTRt39Ct39 It bar chart A 39 39 39 graph of ordinal data frequency polygon A I 39 I ofa f I each histogram box along with connecting lines histogram A I 39 39 of a f of dots at the top center of of 39 boxes It can I be constructed using class frequencies a frequency histogram or relative class frequencies a relative frequency histogram pie chart A statistical graph useful for I 39 a LinearTreml Line 1 bu 171 where t12T b Ely quxxjrw 12E 6T1A Bz t zr r TtTZ l 4 TM 1 1 b J y 1 T 1 2 ofa 39 I quantity where A 2 23 and B Xv y Deseasnnalizerl Value of y d conspm39ld mg seasonal indax39 Simple Aggregate Price Index 3 1 m mallL Measures 0 Central Tendency s cmncmmwnm E 1 ampnmm 5 ox r mu Measures 0 Pnsilinn 2 Poyulatmnmeams Smpm mm 3 Mam15w Measures ulshap LH 2 My 1 human mamas nlskewn Measures niVnriaIinu 1 Rangeis z Samplevananm is Zi rlfa n n4 3 l39ayulatmn mum is a Hm I lmerquamk mngn is 2 anennner 19mm m m nmmmm g m H P h Add H 5Uppelauk rivnceis Q9349 Bivnrinta Dam 1 Sample Wm catX r 2 gram 5 Pnpulmmn sundazd C39V M li n 15 y when X 4 r Ely imrnf standard nsmnm ol thn uniting nndsy devmtmn ml the mm um on a summary of nominal data Histogram to summarize the typing speed of a sample of 100 tex mok editors since it s a graphical summary of ratio dam 9 n l u n n l n with a ratio level of measurement The ol39 39 39 39 discrete data with a ratio measurement A Person WeigitWould be an ex ol39 ordinal dam FALASE Coefficient oivariationstandard deviationmean 100 Exam 1 Review Chapter 1 Skipped Section 17 14 De nitions review 15 Discrete no decimals 7 counting vs continuous has decimals 7 measuring data 16 Level of measurement NOIR Qualitative Data Nominal data Ordinal data Quantitative Data Interval data Ratio data Chapter 2 Skipped Section 27 Class Frequencv Rel Freq frequency distributions Example 10 and under 20 06 20 and under 30 12 12 30 and under 40 22 22 etc Graphs for quantitative data histogram frequency polygon stemandleaf diagram ogive Graphs for qualitative data bar chart pie chart Chapter 3 7 Skipped Sections 37 and 38 Measures for quantitative data measures of central tendency mean median midrange mode measures of variation range variance standard deviation CV measures of position percentiles quartiles zscores measures of shape skewness 7 use Sk 3mean 7 median standard deviation putting the mean and standard deviation together Section 36 handout 7 The Empirical Rule and Chebyshev s Inequality Chapter 4 Skipped Sections 47 48 and 49 Four components of a time series seasonality trend cycles noise Annual data do not have a seasonal component only the remaining three Finding the trend line using annual data b0 b1 b0 is the intercept and b1 is the slope Measuring cyclic activity with annual data the values Y Types of seasonal variation additive vs multiplicative Multiplicative seasonality is the usual situation and is assumed in the KPK macros Example in Section 45 PowerPoint lecture Chapter 4 7 Part 2 Finding the seasonal indexes Determining the deseasonalized data Determining all four components with quarterly or monthly data Section 410 Index Numbers review Laspeyres Index and Paasche index PA and B PA PB provided A and B are independent This means that events A and B don t affect each other This also applies to more than two events PA and B and C PA PB PC provided these three events are independent PA or B PA PB provided A and B are mutually exclusive This means that events A and B cannot both occur This also applies to more than two events PA or B or C PA PB PC provided these three events are mutually exclusive This example is in the textbook A certain community has a morning paper and an evening paper The three pieces of information I 20 of the people take the morning paper I 10 of the people take both PM and E1 lquot39Il H 0 cn o 1 o PM or E 5 40100 4 So 40 of the peop etake ohe paper orthe other Thxs is a cohditioh probahihty PM M 5 1020 5 So 50 of the morhmg subsmhers takethe evehhg paper HArx 9 prohahil39my massfunction P Xxhs The PM here PX 0 5 MW ho morethah meahs S sum ofva ues s ateast meahsz1 6H39sh10fhpsofamhismg 210mm 205 meananxst PM meah0hp variahEeuzhp1p stahdarddewatmhw m ForaPmssohrahdomvahabmtturhsoutthat meah VEHEHEE Any Discrete Random VnriableX Binomial Distrilunion I MM 14 mm 1 x the nmhlwufsummwtufn mdepmdom 2 nmu Eunll m0 Pmu1 m0 Emnnhmwhsmnauimshvnh 3amp4 Us Vw v0 y V 7 WWW 110010 X I39m Mn N lo 1 I Additive mm mm WU wwwwmm Spatial m 1H and B am rmudlymlusfw FtAm39EIPMNEJ 2 Mummiummh n A a 7 n A 1 n n n JUNU WU Special 152 IM and E m Wampum m m 11 PM Pm 2 Candidmul pmbnbihly FlnldE mum Any Discre e RandnnIVariahleX rnww quotp07 p mm sundmd damnmu z pmndAd a mug A1 and mm A 2 Pnissnn Dislrilmlinn 1 2 dm num roicmmnnm on mm event nvsx a main mm ul mm length Men or mum Indspendance39TwuevmhI and 3 m indgpendnmifmm whim lbllawmg ranbuslmwn PM E PIA PM M1131 PM 3 5 Muma y deusive Two Emquot A and E are Axrlume um and u Ls mm m t 4 6 Fusion pmbmhry Bayos Eula Emmy emu r y the mm mm B umumd Rs may mew5me wmm the snmal pmhs Isabtamud bymultiplykng athlemhngmmn summ mg rlw mm over an paths mm a evenk 3 Q 5 2 5 S Einnmial Distrihulinn m cute 11 mdkpmdgm x the numbers sun15 u 15 ammmmm magmas with pmbamrym m nimlum mm pwbnbnln39y 1 rm 1mm PrX x1 an U 4141 V1 2 YMF u my ummy or x 112 3 Meanu 5 Variance u g pmvided m Table A a

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