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Oreface . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 1

Bhapter 1: Rampling and Cata . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . 5

0-0 B_adidodjin ja QoZodnod]n+ Nmj[Z[dgdot+ Zi^ I_t R_mhn - - - - - - , - - - - - - , - - - - - - , - - - - - 4

0-1 BZoZ+ QZhkgdib+ Zi^ TZmdZodji di BZoZ Zi^ QZhkgdib - - - - - - , - - - - - - , - - - - - - , - - - - 8

0-2 Dm_lp_i]t+ Dm_lp_i]t RZ[g_n+ Zi^ J_q_gn ja K_Znpm_h_io - - - - - - , - - - - - - , - - - - - - , 15

0-3 Csk_mdh_ioZg B_ndbi Zi^ Cocd]n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 23

0-4 BZoZ Ajgg_]odji Csk_mdh_io - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - 27

0-5 QZhkgdib Csk_mdh_io - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 3/

Bhapter 2: Cescriptive Rtatistics . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . 65

1-0 Qo_h,Zi^,J_Za EmZkcn 'Qo_hkgjon(+ Jdi_ EmZkcn+ Zi^ ,@Zm EmZkcn - - - - - - , - - - - - - , - - 55

1-1 FdnojbmZhn+ Dm_lp_i]t Njgtbjin+ Zi^ Rdh_ Q_md_n EmZkcn - - - - - - , - - - - - - , - - - - - - , 64

1-2 K_Znpm_n ja oc_ Jj]Zodji ja oc_ BZoZ - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - 74

1-3 @js Ngjon - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 83

1-4 K_Znpm_n ja oc_ A_io_m ja oc_ BZoZ - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , 88

1-5 Qf_ri_nn Zi^ oc_ K_Zi+ K_^dZi+ Zi^ Kj^_ - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 0/4

1-6 K_Znpm_n ja oc_ Qkm_Z^ ja oc_ BZoZ - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,0/8

1-7 B_n]mdkodq_ QoZodnod]n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 008

Bhapter 3: Orobability Sopics . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 165

2-0 R_mhdijgjbt - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,055

2-1 Gi^_k_i^_io Zi^ KpopZggt Cs]gpndq_ Cq_ion - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 06/

2-2 Rrj @Znd] Ppg_n ja Nmj[Z[dgdot - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 066

2-3 Ajiodib_i]t RZ[g_n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 071

2-4 Rm__ Zi^ T_ii BdZbmZhn - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,077

2-5 Nmj[Z[dgdot Rjkd]n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 086

Bhapter 4: Ciscrete Qandom Uariables . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . 227

3-0 Nmj[Z[dgdot Bdnomd[podji Dpi]odji 'NBD( ajm Z Bdn]m_,o_ PZi^jh TZmdZ[g_ - - - - - - , - - - - - 117

3-1 K_Zi jm Csk_]o_^ TZgp_ Zi^ QoZi^Zm^ B_qdZodji - - - - - - , - - - - - - , - - - - - - , - - - - - 12/

3-2 @dijhdZg Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 126

3-3 E_jh_omd] Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 132

3-4 Ftk_mb_jh_omd] Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - 136

3-5 Njdnnji Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 14/

3-6 Bdn]m_o_ Bdnomd[podji 'NgZtdib AZm^ Csk_mdh_io( - - - - - - , - - - - - - , - - - - - - , - - - - - - ,144

3-7 Bdn]m_o_ Bdnomd[podji 'Jp]ft Bd]_ Csk_mdh_io( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 147

Bhapter 5: Bontinuous Qandom Uariables . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -291

4-0 Ajiodipjpn Nmj[Z[dgdot Dpi]odjin - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 182

4-1 Rc_ Sidajmh Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,185

4-2 Rc_ Cskji_iodZg Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - 2/4

4-3 Ajiodipjpn Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,205

Bhapter 6: She Mormal Cistribution . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . 341

5-0 Rc_ QoZi^Zm^ LjmhZg Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 231

5-1 Sndib oc_ LjmhZg Bdnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 236

5-2 LjmhZg Bdnomd[podji 'JZk Rdh_n( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 243

5-3 LjmhZg Bdnomd[podji 'Ndifd_ J_iboc( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,245

Bhapter 7: She Bentral Kimit Sheorem . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . 373

6-0 Rc_ A_iomZg Jdhdo Rc_jm_h ajm QZhkg_ K_Zin '?q_mZb_n( - - - - - - , - - - - - - , - - - - - - ,263

6-1 Rc_ A_iomZg Jdhdo Rc_jm_h ajm Qphn - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - 268

6-2 Sndib oc_ A_iomZg Jdhdo Rc_jm_h - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 272

6-3 A_iomZg Jdhdo Rc_jm_h 'Nj]f_o AcZib_( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - 280

6-4 A_iomZg Jdhdo Rc_jm_h 'Ajjfd_ P_]dk_n( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 283

Bhapter 8: Bonfidence Hntervals . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -415

7-0 ? Qdibg_ NjkpgZodji K_Zi pndib oc_ LjmhZg Bdnomd[po,dji - - - - - - , - - - - - - , - - - - - - , - 306

7-1 ? Qdibg_ NjkpgZodji K_Zi pndib oc_ Qop^_io o Bdn,omd[podji - - - - - - , - - - - - - , - - - - - - ,316

7-2 ? NjkpgZodji Nmjkjmodji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,320

7-3 Ajiad^_i]_ Gio_mqZg 'Fjh_ Ajnon( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 327

7-4 Ajiad^_i]_ Gio_mqZg 'NgZ]_ ja @dmoc( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - ,33/

7-5 Ajiad^_i]_ Gio_mqZg 'Ujh_i&n F_dbcon( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - 331

Bhapter 9: Gypothesis Sesting with Nne Rample . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 473

8-0 Lpgg Zi^ ?go_miZodq_ Ftkjoc_n_n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 363

8-1 Mpo]jh_n Zi^ oc_ Rtk_ G Zi^ Rtk_ GG Cmmjmn - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 365

8-2 Bdnomd[podji L__^_^ ajm Ftkjoc_ndn R_nodib - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 367

8-3 PZm_ Cq_ion+ oc_ QZhkg_+ B_]dndji Zi^ Aji]gpndji - - - - - - , - - - - - - , - - - - - - , - - - - 368

9.5 Additional Information and Full Hypothesis Test Examples . . . . . . - . . . . . . - . . . . . . -482

9.6 Hypothesis Testing of a Single Mean and Single Proportion . . . . . . - . . . . . . - . . . . . . -498

@cZko_m 0/9 Etkjoc_ndn Q_nodib rdoc Qrj PZhkg_n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 418

10.1 Two Population Means with Unknown Standard Deviations . . . . . . - . . . . . . - . . . . . 530

10.2 Two Population Means with Known Standard Deviations . . . . . . - . . . . . . - . . . . . . -538

10.3 Comparing Two Independent Population Proportions . . . . . . - . . . . . . - . . . . . . - . . 541

10.4 Matched or Paired Samples . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 545

10.5 Hypothesis Testing for Two Means and Two Proportions . . . . . . - . . . . . . - . . . . . . - . 551

@cZko_m 009 Qc_ @cd,PlpZm_ Adnomd[podji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 470

11.1 Facts About the Chi-Square Distribution . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 582

11.2 Goodness-of-Fit Test . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 583

11.3 Test of Independence . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 592

11.4 Test for Homogeneity . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 596

11.5 Comparison of the Chi-Square Tests . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . 599

11.6 Test of a Single Variance . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . 600

11.7 Lab 1: Chi-Square Goodness-of-Fit . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -602

11.8 Lab 2: Chi-Square Test of Independence . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 606

@cZko_m 019 Idi_Zm O_bm_nndji Zi^ @jmm_gZodji - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 526

12.1 Linear Equations . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 638

12.2 Scatter Plots . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . 640

12.3 The Regression Equation . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . 643

12.4 Testing the Significance of the Correlation Coefficient . . . . . . - . . . . . . - . . . . . . - . . 649

12.5 Prediction . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 654

12.6 Outliers . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . 655

12.7 Regression (Distance from School) . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -663

12.8 Regression (Textbook Cost) . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 665

12.9 Regression (Fuel Efficiency) . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 667

@cZko_m 029 C Adnomd[podji Zi^ Li_,TZt >KLS> - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 588

13.1 One-Way ANOVA . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . 700

13.2 The F Distribution and the F-Ratio . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -701

13.3 Facts About the F Distribution . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . 705

13.4 Test of Two Variances . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . 712

13.5 Lab: One-Way ANOVA . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . - . . . . . . -715

>kk_i^ds >9 O_qd_r Bs_m]dn_n '@c 2,02,( - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 628

>kk_i^ds ?9 MmZ]od]_ Q_non '0,3( Zi^ CdiZg BsZhn - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 654

>kk_i^ds @9 AZoZ P_on - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - 708

>kk_i^ds A9 Dmjpk Zi^ MZmoi_m Mmje_]on - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 712

>kk_i^ds B9 Pjgpodji Pc__on - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - 718

>kk_i^ds C9 JZoc_hZod]Zg McmZn_n+ Pth[jgn+ Zi^ ,CjmhpgZn - - - - - - , - - - - - - , - - - - - - , - - 722

>kk_i^ds D9 Kjo_n ajm oc_ QF,72+ 72,*+ 73+ 73* @Zg]pgZojmn - - - - - - , - - - - - - , - - - - - - , - - - - 728

>kk_i^ds E9 QZ[g_n - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - 740

Fi^_s - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - - - - , - - - 742This content is available for free at https:/-//content/col11562/1.17

PREFACE

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Adam Pennell Greensboro College

Alexander Kolovos

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Ann Flanigan Kapiolani Community College

Benjamin Ngwudike Jackson State University

Birgit Aquilonius West Valley College

Bryan Blount Kentucky Wesleyan College

Carol Olmstead De Anza College

Carol Weideman St. Petersburg College

Charles Ashbacher Upper Iowa University, Cedar Rapids

Charles Klein De Anza College

Cheryl Wartman University of Prince Edward Island

Cindy Moss Skyline College

Daniel Birmajer Nazareth College

David Bosworth Hutchinson Community College

David French Tidewater Community College

Dennis Walsh Middle Tennessee State University

Diane Mathios De Anza College2

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Frank Snow De Anza College

George Bratton University of Central Arkansas

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Javier Rueda De Anza College

Jeffery Taub Maine Maritime Academy

Jim Helmreich Marist College

Jim LucasDe Anza College

Jing Chang College of Saint Mary

John Thomas College of Lake County

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Laurel Chiappetta University of Pittsburgh

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Lynette Kenyon Collin County Community College

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Mary Teegarden San Diego Mesa College

Matthew Einsohn Prescott College

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Michael Greenwich College of Southern Nevada

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Mo Geraghty De Anza College

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Philip J. Verrecchia York College of Pennsylvania

Robert Henderson Stephen F. Austin State University

Robert McDevitt Germanna Community College

Roberta Bloom De Anza College

Rupinder Sekhon De Anza College

Sara Lenhart Christopher Newport University

Sarah Boslaugh Kennesaw State University

Sheldon Lee Viterbo University

Sheri Boyd Rollins College

Sudipta Roy Kankakee Community College

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Jey Serms

In statistics, we generally want to study aFEFKB7J?ED. You can think of a population as a collection of persons, things, or

objects under study. To study the population, we select aI7CFB;. The idea ofI7CFB?D=is to select a portion (or subset)

of the larger population and study that portion (the sample) to gain information about the population. Data are the result of

sampling from a population.

Because it takes a lot of time and money to examine an entire population, sampling is a very practical technique. If you

wished to compute the overall grade point average at your school, it would make sense to select a sample of students who

attend the school. The data collected from the sample would be the students' grade point averages. In presidential elections,

opinion poll samples of 1,000-2,000 people are taken. The opinion poll is supposed to represent the views of the people

in the entire country. Manufacturers of canned carbonated drinks take samples to determine if a 16 ounce can contains 16

ounces of carbonated drink.

From the sample data, we can calculate a statistic. AIJ7J?IJ?9is a number that represents a property of the sample. For

example, if we consider one math class to be a sample of the population of all math classes, then the average number of

points earned by students in that one math class at the end of the term is an example of a statistic. The statistic is an estimate

of a population parameter. AF7H7C;J;His a number that is a property of the population. Since we considered all math

classes to be the population, then the average number of points earned per student over all the math classes is an example

of a parameter.

One of the main concerns in the field of statistics is how accurately a statistic estimates a parameter. The accuracy really

depends on how well the sample represents the population. The sample must contain the characteristics of the population

in order to be aH;FH;I;DJ7J?L; I7CFB;. We are interested in both the sample statistic and the population parameter in

inferential statistics. In a later chapter, we will use the sample statistic to test the validity of the established population

parameter.

AL7H?78B;, notated by capital letters such asXandY, is a characteristic of interest for each person or thing in a population.

Variables may beDKC;H?97Bor97J;=EH?97B.μKC;H?97B L7H?78B;Itake on values with equal units such as weight in pounds

and time in hours.n7J;=EH?97B L7H?78B;Iplace the person or thing into a category. If we letXequal the number of points

earned by one math student at the end of a term, thenXis a numerical variable. If we letYbe a person's party affiliation,

then some examples ofYinclude Republican, Democrat, and Independent.Yis a categorical variable. We could do some

math with values ofX(calculate the average number of points earned, for example), but it makes no sense to do math with

values ofY(calculating an average party affiliation makes no sense).

p7J7are the actual values of the variable. They m,ay be numbers or they may be words.p7JKCis a single value.

Two words that come up often in statistics areC;7DandFHEFEHJ?ED. If you were to take three exams in your math classes

and obtain scores of 86, 75, and 92, you would calculate your mean score by adding the three exam scores and dividing by

three (your mean score would be 84.3 to one decimal place). If, in your math class, there are 40 students and 22 are men

and 18 are women, then the proportion of men students is @MC SGD OQNONQSHNM NE VNLDM RSTCDMSR HR .D@M @MC MNSD

The words "C;7D" and "7L;H7=;" are often used interchangeably. The substitution of one word for the other is

common practice. The technical term is "arithmetic mean," and "average" is technically a center location. However, in

practice among non-statisticians, "average" is ,commonly accepted for "arithmetic mean." $R;GJF?d

Determine what the key terms refer to in the following study. We want to know the average (mean) amount

of money first year college students spend at ABC College on school supplies that do not include books. We

randomly survey 100 first year students at the college. Three of those students spent $150, $200, and $225,

quotesdbs_dbs20.pdfusesText_26