SlideShare a Scribd company logo
Statistics 2 Dr. Ning DING IBS I.007 [email_address]   You’d better use the full-screen mode to view this PPT file.
Table of Contents Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Sampling and Sampling Distribution Population = all items chosen for study Sample = a portion chosen from the population Parameter Statistic    Greek or capital letters    Lowercase Roman letters Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Sampling and Sampling Distribution Population Sample Parameter Statistic N = number μ  = mean σ  = standard deviation n = number X = mean SD = standard deviation Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Sampling Distribution Mean Mean Mean Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Standard Error = Standard deviation of the distribution of a sample statistic Larger Standard Error Smaller  Standard Error Which one is better? Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Standard Error Sample size  Dispersion of sample means Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Standard Error µ = 100 σ  = 25 =95 =106 =101 Population Range=80~240 Sample Range=90~120 Standard Error of mean Standard Deviation of population ____ Chapter 6 Sampling - Review: Sampling and Standard Error -  Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test Sample means frequency
Calculating the Standard Error individual savings accounts  µ= $2000 σ = $600 Sample=  100 accounts the probability that the sample mean lies betw. $1900~$2050 ? Standard Error of the mean Population standard deviation Sample size Example: Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error the probability that the sample mean lies betw. $1900~$2050 ? Sample mean Population mean Standard error of the mean 0.4525 0.2967 + = 0.7492 74.92% of our sample means lies betw. $1900~$2050 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error 6-30  Chapter 6, No. 6-30 P.321 Known: Normal distribution,  μ =375  σ =48  P=95% n = ? Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error 6-30  Chapter 6, No. 6-30 P.321 Known: Normal distribution,  μ =375  σ =48  P=95%  n=? Step 1: P=P z1 +P z2 =0.950 z 1 =-1.96  z 2 =1.96 370<  <380 -1.96<  z  <1.96 Step 2: 1.96= Step 3: n=354.04 The sample size is at least 355 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error Infinite population  Finite population Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
The Finite Population Multiplier population size sample size Finite population multiplier F.P.M. Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
The Finite Population Multiplier 1) N= 20  n=5 0.888 2) N= 20  n=19 0.229 3) N= 20  n=20 0 4) N= 1000  n=20 0.99 When to use F.P.M.?  If  Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error SC 6-7a Chapter 6, SC No. 6-7 P.327 Known: N=125  n=64  μ =105  σ =17  =? Step 1: n/N=64/125= 0.512  >0.05 Yes, it is allowed to use F.P.M. Step 2:  = = = Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error SC 6-7b Chapter 6, SC No. 6-7 P.327 Known: N=125  n=64  μ =105  σ =17  =1.4904 P(107.5<Xmean<109) = ? Step 1: visualize and calculate z scores = = P 1 =0.4535 P 2 =0.4963 P=0.4963-0.4535=0.0428 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error SC 6-8 Chapter 6, SC No. 6-8 P.327 Known: n=36  μ =?  σ =1.25 pounds What is the probability that the sample mean is within one-half pound of the population mean? = = Step 1: Visualize and calculate z scores Step 2: Calculate the standard error of sample means Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Calculating the Standard Error SC 6-8 Chapter 6, SC No. 6-8 P.327 Known: n=36  μ =?  σ =1.25 pounds What is the probability that the sample mean is within one-half pound of the population mean? = = Step 2: Calculate the standard error of sample means Step 3: Calculate the z scores P z1 =0.4918 P z2 =0.4918 + = 0.9836 Step 4: convert to P value Step 5: Finalize your answer Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population -  Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Chapter 7 Introduction of Estimation confidence level confidence interval Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Types of Estimates Interval  Estimates Point Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts P.354 Interval  Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months Stardard error of the mean from an infinite population standard deviation of the population sample size 36+0.707=36.707 36-0.707=35.293 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts P.354 Interval  Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =1.0  P=0.3413 68.3% of the actual mean lie between 35.293 and 36.707 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts P.354 Interval  Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =2.0  P=0.4775 95.5% of the actual mean lie between _______  and_________ 95.5% of the actual mean lie between 34.586 and 37.414 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts P.354 Interval  Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =3.0  P=0.4987 99.7% of the actual mean lie between 33.879 and 38.121 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts P.354 Interval  Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts Interval  Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test Interval =
Interval Estimates:  Basic Concepts Interval  Estimates EX 7-27a Chapter 7, No. 7-27 P. 365 Known: n=40 EX 7-27b P=90%   z=1.645 Upper limit =1416+7.8029 Lower limit=1416-7.8029 =1424 =1408 90% confident that our population mean lies between 1408 and 1424.  Interval  Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test = Interval =
Interval Estimates:  Basic Concepts Interval  Estimates If the  σ  is  unknown , ? Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test Estimated standard error of  mean Estimated standard error of  proportion P.368 Interval =  Interval =  Interval =
Interval Estimates:  Basic Concepts Interval  Estimates EX 7-35a EX 7-35b z=2.33 Answer: 0.01 ~0.09 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test Chapter 7, No. 7-35 P. 369 known: n=200  p=0.05  q=0.95 known: n=200  p=0.05  q=0.95  P=98% Interval =  
Interval Estimates:  Basic Concepts Interval  Estimates If the sample size is =< 30, AND  σ  is unknown,  ? t - distribution You can read the t value from Appendix Table 2 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts Interval  Estimates How to read the t-table ? t - distribution e.g. n=10     df= 9  P=90%  0.05 0.05 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test
Interval Estimates:  Basic Concepts Interval  Estimates How to use t value ? t - distribution Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation -  Types of Estimates - Interval Estimates SPSS Tips for t-test interval = interval =
Summary Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter  7 Introduction of Estimation - Types of Estimates - Interval Estimates
The Normal Distribution SPSS Tip: t-test The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3
The Normal Distribution SPSS Tip: t-test 3 types of t-test One Sample t-test Paired-Samples t-test Independent Samples t-test test whether the population mean is different from a constant test whether the population mean of differences between paired scores is equal to zero test the relationship between two categories and a quantitative variable
The Normal Distribution SPSS Tip: t-test One Sample t-test Example: A researcher wants to evaluate whether customers believe  price change  is more a function of natural fluctuations in inflation or due to effects caused by human interventions. Thirty customers are assessed on the  Price Change Attitude Scale , which yields scores that range from 0 ( due solely to natural fluctuations in inflation ) to 100 ( due solely to human interventions ). A score of  50  is the  test value  and represents an equal contribution of the two effects.  The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3 One-Sample t-test.sav Variable Description PCAS Price Change Attitude Scale
The Normal Distribution SPSS Tip: t-test One Sample t-test Example: A researcher wants to evaluate whether customers believe  price change  is more a function of natural fluctuations in inflation or due to effects caused by human interventions. Thirty customers are assessed on the  Price Change Attitude Scale , which yields scores that range from 0 ( due solely to natural fluctuations in inflation ) to 100 ( due solely to human interventions ). A score of  50  is the  test value  and represents an equal contribution of the two effects.  Null Hypothesis:  The population mean is equal to 50. Variable Description PCAS Price Change Attitude Scale
The Normal Distribution SPSS Tip: t-test Step 1: Choose Analyze--> Compare Means --> One-Sample T Test One Sample t-test
The Normal Distribution SPSS Tip: t-test Step 2: Move the variable you want to test into the box ”Test Variable(s)”. Enter the value in the box “Test Value”. In this example, the PCAS middle value is 50. Click OK and you will see a popup window.  One Sample t-test
The Normal Distribution SPSS Tip: t-test One Sample t-test Read the next slide to know how to interpret it !
The Normal Distribution SPSS Tip: t-test ,[object Object],[object Object],[object Object],[object Object],One Sample t-test
The Normal Distribution SPSS Tip: t-test Example: A researcher is interested in determining whether customers’ satisfaction with DOVE body lotion improves when exposed to a new TV commercial. Thirty customers are assessed on the  Satisfaction Scale for Customers (SSC)  prior to  and  after  the new TV commercial.  The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3 Paired-Sample t-test.sav Paired-Samples t-test Variable Description Pre_SSC Percent correct on the Sales Scale for Customers prior to the new TV commercial Post_SSC Percent correct on the Sales Scale for Customers after the new TV commercial
The Normal Distribution SPSS Tip: t-test Example: A researcher is interested in determining whether customers’ satisfaction with DOVE body lotion improves when exposed to a new TV commercial. Thirty customers are assessed on the  Satisfaction Scale for Customers (SSC)  prior to  and  after  the new TV commercial.  Paired-Samples t-test Null Hypothesis:  The population means’ difference is zero. Variable Description Pre_SSC Percent correct on the Sales Scale for Customers prior to the new TV commercial Post_SSC Percent correct on the Sales Scale for Customers after the new TV commercial
The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Step 1: Choose Analyze--> Compare Means --> Paired-Samples T Test
The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Step 2: Move the first variable the box “Paired Variables”, to the location Variable 1, and the second variable to Variable 2. Click OK and you will see a popup window.
The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Read the next slide to know how to interpret it !
The Normal Distribution SPSS Tip: t-test Paired-Samples t-test ,[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Parametric and non parametric test
Parametric and non parametric testParametric and non parametric test
Parametric and non parametric test
Ajay Malpani
 
Estimation and hypothesis testing 1 (graduate statistics2)
Estimation and hypothesis testing 1 (graduate statistics2)Estimation and hypothesis testing 1 (graduate statistics2)
Estimation and hypothesis testing 1 (graduate statistics2)
Harve Abella
 
One Sample T Test
One Sample T TestOne Sample T Test
One Sample T Test
shoffma5
 
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptxSampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
DrSindhuAlmas
 
Commonly used statistical tests in research
Commonly used statistical tests in researchCommonly used statistical tests in research
Commonly used statistical tests in research
Naqeeb Ullah Khan
 
Standard error-Biostatistics
Standard error-BiostatisticsStandard error-Biostatistics
Standard error-Biostatistics
Sudha Rameshwari
 
Granger causality testing
Granger causality testingGranger causality testing
Granger causality testing
ThomasReader
 
Parametric test
Parametric testParametric test
In Anova
In  AnovaIn  Anova
In Anova
ahmad bassiouny
 
Research method ch08 statistical methods 2 anova
Research method ch08 statistical methods 2 anovaResearch method ch08 statistical methods 2 anova
Research method ch08 statistical methods 2 anova
naranbatn
 
Cluster sampling
Cluster samplingCluster sampling
Cluster sampling
shahilashahul
 
types of hypothesis
types of hypothesistypes of hypothesis
types of hypothesis
Zahra Naz
 
Critical Value and The P Value
Critical Value and The P ValueCritical Value and The P Value
Critical Value and The P Value
Pharmacy Universe
 
Nested case control,
Nested case control,Nested case control,
Nested case control,
shefali jain
 
Sample size estimation
Sample size estimationSample size estimation
Sample size estimation
HanaaBayomy
 
Survival analysis
Survival  analysisSurvival  analysis
The sampling distribution
The sampling distributionThe sampling distribution
The sampling distribution
Harve Abella
 
z-test
z-testz-test
Randomized control trial
Randomized control trialRandomized control trial
Randomized control trial
BPKIHS
 
Roc curves
Roc curvesRoc curves
Roc curves
Prayas Gautam
 

What's hot (20)

Parametric and non parametric test
Parametric and non parametric testParametric and non parametric test
Parametric and non parametric test
 
Estimation and hypothesis testing 1 (graduate statistics2)
Estimation and hypothesis testing 1 (graduate statistics2)Estimation and hypothesis testing 1 (graduate statistics2)
Estimation and hypothesis testing 1 (graduate statistics2)
 
One Sample T Test
One Sample T TestOne Sample T Test
One Sample T Test
 
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptxSampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
 
Commonly used statistical tests in research
Commonly used statistical tests in researchCommonly used statistical tests in research
Commonly used statistical tests in research
 
Standard error-Biostatistics
Standard error-BiostatisticsStandard error-Biostatistics
Standard error-Biostatistics
 
Granger causality testing
Granger causality testingGranger causality testing
Granger causality testing
 
Parametric test
Parametric testParametric test
Parametric test
 
In Anova
In  AnovaIn  Anova
In Anova
 
Research method ch08 statistical methods 2 anova
Research method ch08 statistical methods 2 anovaResearch method ch08 statistical methods 2 anova
Research method ch08 statistical methods 2 anova
 
Cluster sampling
Cluster samplingCluster sampling
Cluster sampling
 
types of hypothesis
types of hypothesistypes of hypothesis
types of hypothesis
 
Critical Value and The P Value
Critical Value and The P ValueCritical Value and The P Value
Critical Value and The P Value
 
Nested case control,
Nested case control,Nested case control,
Nested case control,
 
Sample size estimation
Sample size estimationSample size estimation
Sample size estimation
 
Survival analysis
Survival  analysisSurvival  analysis
Survival analysis
 
The sampling distribution
The sampling distributionThe sampling distribution
The sampling distribution
 
z-test
z-testz-test
z-test
 
Randomized control trial
Randomized control trialRandomized control trial
Randomized control trial
 
Roc curves
Roc curvesRoc curves
Roc curves
 

Viewers also liked

Les5e ppt 07
Les5e ppt 07Les5e ppt 07
Les5e ppt 07
Subas Nandy
 
Les5e ppt 08
Les5e ppt 08Les5e ppt 08
Les5e ppt 08
Subas Nandy
 
Reliability & validity
Reliability & validityReliability & validity
Reliability & validity
alameenpa
 
Dynamic Factor Rotation
Dynamic Factor RotationDynamic Factor Rotation
Dynamic Factor Rotation
Ilan Gleiser
 
Level of Measurement, Frequency Distribution,Stem & Leaf
Level of Measurement, Frequency Distribution,Stem & Leaf   Level of Measurement, Frequency Distribution,Stem & Leaf
Level of Measurement, Frequency Distribution,Stem & Leaf
Qasim Raza
 
Measurement
MeasurementMeasurement
Measurement
wilsone
 
Lecture 3 measurement, reliability and validity (
Lecture   3 measurement, reliability and validity (Lecture   3 measurement, reliability and validity (
Lecture 3 measurement, reliability and validity (
La Islaa
 
Les5e ppt 06
Les5e ppt 06Les5e ppt 06
Les5e ppt 06
Subas Nandy
 
Reliability & validity
Reliability & validityReliability & validity
Reliability & validity
shefali84
 
Identify variable and measurement of scale
Identify variable and measurement of scaleIdentify variable and measurement of scale
Identify variable and measurement of scale
Janisha Gandhi
 
Priya
PriyaPriya
Priya
Student
 
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
Mahvesh Zahra
 
Multivariate data analysis
Multivariate data analysisMultivariate data analysis
Multivariate data analysis
Setia Pramana
 
Statistics for-management-by-levin-and-rubin-solution-manual 2
Statistics for-management-by-levin-and-rubin-solution-manual 2Statistics for-management-by-levin-and-rubin-solution-manual 2
Statistics for-management-by-levin-and-rubin-solution-manual 2
ShamimSiddik
 
Measurement And Error
Measurement And ErrorMeasurement And Error
Measurement And Error
wilsone
 
Errors in measurement
Errors in measurementErrors in measurement
Errors in measurement
Ravinder Jarewal
 
Ge273.u10.pp1
Ge273.u10.pp1Ge273.u10.pp1
Ge273.u10.pp1
Subas Nandy
 
Correlation
CorrelationCorrelation
Correlation
James Neill
 
Multivariate Analysis An Overview
Multivariate Analysis An OverviewMultivariate Analysis An Overview
Multivariate Analysis An Overview
guest3311ed
 
Multivariate Analysis Techniques
Multivariate Analysis TechniquesMultivariate Analysis Techniques
Multivariate Analysis Techniques
Mehul Gondaliya
 

Viewers also liked (20)

Les5e ppt 07
Les5e ppt 07Les5e ppt 07
Les5e ppt 07
 
Les5e ppt 08
Les5e ppt 08Les5e ppt 08
Les5e ppt 08
 
Reliability & validity
Reliability & validityReliability & validity
Reliability & validity
 
Dynamic Factor Rotation
Dynamic Factor RotationDynamic Factor Rotation
Dynamic Factor Rotation
 
Level of Measurement, Frequency Distribution,Stem & Leaf
Level of Measurement, Frequency Distribution,Stem & Leaf   Level of Measurement, Frequency Distribution,Stem & Leaf
Level of Measurement, Frequency Distribution,Stem & Leaf
 
Measurement
MeasurementMeasurement
Measurement
 
Lecture 3 measurement, reliability and validity (
Lecture   3 measurement, reliability and validity (Lecture   3 measurement, reliability and validity (
Lecture 3 measurement, reliability and validity (
 
Les5e ppt 06
Les5e ppt 06Les5e ppt 06
Les5e ppt 06
 
Reliability & validity
Reliability & validityReliability & validity
Reliability & validity
 
Identify variable and measurement of scale
Identify variable and measurement of scaleIdentify variable and measurement of scale
Identify variable and measurement of scale
 
Priya
PriyaPriya
Priya
 
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
Statistics for-management-by-levin-and-rubin-solution-manual2-130831111553-ph...
 
Multivariate data analysis
Multivariate data analysisMultivariate data analysis
Multivariate data analysis
 
Statistics for-management-by-levin-and-rubin-solution-manual 2
Statistics for-management-by-levin-and-rubin-solution-manual 2Statistics for-management-by-levin-and-rubin-solution-manual 2
Statistics for-management-by-levin-and-rubin-solution-manual 2
 
Measurement And Error
Measurement And ErrorMeasurement And Error
Measurement And Error
 
Errors in measurement
Errors in measurementErrors in measurement
Errors in measurement
 
Ge273.u10.pp1
Ge273.u10.pp1Ge273.u10.pp1
Ge273.u10.pp1
 
Correlation
CorrelationCorrelation
Correlation
 
Multivariate Analysis An Overview
Multivariate Analysis An OverviewMultivariate Analysis An Overview
Multivariate Analysis An Overview
 
Multivariate Analysis Techniques
Multivariate Analysis TechniquesMultivariate Analysis Techniques
Multivariate Analysis Techniques
 

Similar to Lesson 03 chapter 6 sampling

Paranormal statistics: Counting What Doesn't Add Up
Paranormal statistics: Counting What Doesn't Add UpParanormal statistics: Counting What Doesn't Add Up
Paranormal statistics: Counting What Doesn't Add Up
Workhorse Computing
 
Kaedah Menganalisis data/Data Analysis
Kaedah Menganalisis data/Data AnalysisKaedah Menganalisis data/Data Analysis
Kaedah Menganalisis data/Data Analysis
Universiti Pendidikan Sultan Idris
 
Capstone Project - Nicholas Imholte - Final Draft
Capstone Project - Nicholas Imholte - Final DraftCapstone Project - Nicholas Imholte - Final Draft
Capstone Project - Nicholas Imholte - Final Draft
Nick Imholte
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
Krunal Shah
 
Sampling
SamplingSampling
A05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat TestsA05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat Tests
Leanleaders.org
 
A05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat TestsA05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat Tests
Leanleaders.org
 
Lab manual_statistik
Lab manual_statistikLab manual_statistik
Lab manual_statistik
Nur Afny Andryani
 
Pengenalan Ekonometrika
Pengenalan EkonometrikaPengenalan Ekonometrika
Pengenalan Ekonometrika
XYZ Williams
 
Sampling
SamplingSampling
Sampling
al amin
 
LR 9 Estimation.pdf
LR 9 Estimation.pdfLR 9 Estimation.pdf
LR 9 Estimation.pdf
giovanniealvarez1
 
Normal Distribution
Normal DistributionNormal Distribution
Normal Distribution
Shubham Mehta
 
Lecture 4 Applied Econometrics and Economic Modeling
Lecture 4 Applied Econometrics and Economic ModelingLecture 4 Applied Econometrics and Economic Modeling
Lecture 4 Applied Econometrics and Economic Modeling
stone55
 
Test of hypotheses part ii
Test of hypotheses part iiTest of hypotheses part ii
Test of hypotheses part ii
Hamdy F. F. Mahmoud
 
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
Quinn Lathrop
 
BBS10_ppt_ch07_Sampling_Distribution.ppt
BBS10_ppt_ch07_Sampling_Distribution.pptBBS10_ppt_ch07_Sampling_Distribution.ppt
BBS10_ppt_ch07_Sampling_Distribution.ppt
Hermita3
 
Accurate Campaign Targeting Using Classification - Poster
Accurate Campaign Targeting Using Classification - PosterAccurate Campaign Targeting Using Classification - Poster
Accurate Campaign Targeting Using Classification - Poster
Jieming Wei
 
NTU DBME5028 Week5 Introduction to Machine Learning
NTU DBME5028 Week5 Introduction to Machine Learning NTU DBME5028 Week5 Introduction to Machine Learning
NTU DBME5028 Week5 Introduction to Machine Learning
Sean Yu
 
Sqqs1013 ch6-a122
Sqqs1013 ch6-a122Sqqs1013 ch6-a122
Sqqs1013 ch6-a122
kim rae KI
 
Lesson 05 chapter 8 hypothesis testing
Lesson 05 chapter 8 hypothesis testingLesson 05 chapter 8 hypothesis testing
Lesson 05 chapter 8 hypothesis testing
Ning Ding
 

Similar to Lesson 03 chapter 6 sampling (20)

Paranormal statistics: Counting What Doesn't Add Up
Paranormal statistics: Counting What Doesn't Add UpParanormal statistics: Counting What Doesn't Add Up
Paranormal statistics: Counting What Doesn't Add Up
 
Kaedah Menganalisis data/Data Analysis
Kaedah Menganalisis data/Data AnalysisKaedah Menganalisis data/Data Analysis
Kaedah Menganalisis data/Data Analysis
 
Capstone Project - Nicholas Imholte - Final Draft
Capstone Project - Nicholas Imholte - Final DraftCapstone Project - Nicholas Imholte - Final Draft
Capstone Project - Nicholas Imholte - Final Draft
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
 
Sampling
SamplingSampling
Sampling
 
A05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat TestsA05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat Tests
 
A05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat TestsA05 Continuous One Variable Stat Tests
A05 Continuous One Variable Stat Tests
 
Lab manual_statistik
Lab manual_statistikLab manual_statistik
Lab manual_statistik
 
Pengenalan Ekonometrika
Pengenalan EkonometrikaPengenalan Ekonometrika
Pengenalan Ekonometrika
 
Sampling
SamplingSampling
Sampling
 
LR 9 Estimation.pdf
LR 9 Estimation.pdfLR 9 Estimation.pdf
LR 9 Estimation.pdf
 
Normal Distribution
Normal DistributionNormal Distribution
Normal Distribution
 
Lecture 4 Applied Econometrics and Economic Modeling
Lecture 4 Applied Econometrics and Economic ModelingLecture 4 Applied Econometrics and Economic Modeling
Lecture 4 Applied Econometrics and Economic Modeling
 
Test of hypotheses part ii
Test of hypotheses part iiTest of hypotheses part ii
Test of hypotheses part ii
 
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
Making Statistics Work For Us: Item Bias, Decision Making, and Data-Driven Si...
 
BBS10_ppt_ch07_Sampling_Distribution.ppt
BBS10_ppt_ch07_Sampling_Distribution.pptBBS10_ppt_ch07_Sampling_Distribution.ppt
BBS10_ppt_ch07_Sampling_Distribution.ppt
 
Accurate Campaign Targeting Using Classification - Poster
Accurate Campaign Targeting Using Classification - PosterAccurate Campaign Targeting Using Classification - Poster
Accurate Campaign Targeting Using Classification - Poster
 
NTU DBME5028 Week5 Introduction to Machine Learning
NTU DBME5028 Week5 Introduction to Machine Learning NTU DBME5028 Week5 Introduction to Machine Learning
NTU DBME5028 Week5 Introduction to Machine Learning
 
Sqqs1013 ch6-a122
Sqqs1013 ch6-a122Sqqs1013 ch6-a122
Sqqs1013 ch6-a122
 
Lesson 05 chapter 8 hypothesis testing
Lesson 05 chapter 8 hypothesis testingLesson 05 chapter 8 hypothesis testing
Lesson 05 chapter 8 hypothesis testing
 

More from Ning Ding

Victor Yuan: interpretation of the economic data in China
Victor Yuan: interpretation of the economic data in ChinaVictor Yuan: interpretation of the economic data in China
Victor Yuan: interpretation of the economic data in China
Ning Ding
 
Lesson 6
Lesson 6Lesson 6
Lesson 6
Ning Ding
 
Lesson 5
Lesson 5Lesson 5
Lesson 5
Ning Ding
 
Lesson 4
Lesson 4Lesson 4
Lesson 4
Ning Ding
 
Lesson 3
Lesson 3Lesson 3
Lesson 3
Ning Ding
 
Lesson 2
Lesson 2Lesson 2
Lesson 2
Ning Ding
 
Lesson 1
Lesson 1Lesson 1
Lesson 1
Ning Ding
 
Lesson 06 chapter 9 two samples test and Chapter 11 chi square test
Lesson 06 chapter 9 two samples test and Chapter 11 chi square testLesson 06 chapter 9 two samples test and Chapter 11 chi square test
Lesson 06 chapter 9 two samples test and Chapter 11 chi square test
Ning Ding
 
Lesson 04 chapter 7 estimation
Lesson 04 chapter 7 estimationLesson 04 chapter 7 estimation
Lesson 04 chapter 7 estimation
Ning Ding
 
Oct11 college 5
Oct11 college 5Oct11 college 5
Oct11 college 5
Ning Ding
 
Sept27 college 3
Sept27 college 3Sept27 college 3
Sept27 college 3
Ning Ding
 
Sept19 college 2
Sept19 college 2Sept19 college 2
Sept19 college 2
Ning Ding
 
Lesson 02 class practices
Lesson 02 class practicesLesson 02 class practices
Lesson 02 class practices
Ning Ding
 
Sept13 2011 college 1
Sept13 2011 college 1Sept13 2011 college 1
Sept13 2011 college 1
Ning Ding
 
Lesson01
Lesson01Lesson01
Lesson01
Ning Ding
 
Lesson06
Lesson06Lesson06
Lesson06
Ning Ding
 
Lesson05
Lesson05Lesson05
Lesson05
Ning Ding
 
Lesson04
Lesson04Lesson04
Lesson04
Ning Ding
 
Lesson03
Lesson03Lesson03
Lesson03
Ning Ding
 
Lesson02
Lesson02Lesson02
Lesson02
Ning Ding
 

More from Ning Ding (20)

Victor Yuan: interpretation of the economic data in China
Victor Yuan: interpretation of the economic data in ChinaVictor Yuan: interpretation of the economic data in China
Victor Yuan: interpretation of the economic data in China
 
Lesson 6
Lesson 6Lesson 6
Lesson 6
 
Lesson 5
Lesson 5Lesson 5
Lesson 5
 
Lesson 4
Lesson 4Lesson 4
Lesson 4
 
Lesson 3
Lesson 3Lesson 3
Lesson 3
 
Lesson 2
Lesson 2Lesson 2
Lesson 2
 
Lesson 1
Lesson 1Lesson 1
Lesson 1
 
Lesson 06 chapter 9 two samples test and Chapter 11 chi square test
Lesson 06 chapter 9 two samples test and Chapter 11 chi square testLesson 06 chapter 9 two samples test and Chapter 11 chi square test
Lesson 06 chapter 9 two samples test and Chapter 11 chi square test
 
Lesson 04 chapter 7 estimation
Lesson 04 chapter 7 estimationLesson 04 chapter 7 estimation
Lesson 04 chapter 7 estimation
 
Oct11 college 5
Oct11 college 5Oct11 college 5
Oct11 college 5
 
Sept27 college 3
Sept27 college 3Sept27 college 3
Sept27 college 3
 
Sept19 college 2
Sept19 college 2Sept19 college 2
Sept19 college 2
 
Lesson 02 class practices
Lesson 02 class practicesLesson 02 class practices
Lesson 02 class practices
 
Sept13 2011 college 1
Sept13 2011 college 1Sept13 2011 college 1
Sept13 2011 college 1
 
Lesson01
Lesson01Lesson01
Lesson01
 
Lesson06
Lesson06Lesson06
Lesson06
 
Lesson05
Lesson05Lesson05
Lesson05
 
Lesson04
Lesson04Lesson04
Lesson04
 
Lesson03
Lesson03Lesson03
Lesson03
 
Lesson02
Lesson02Lesson02
Lesson02
 

Recently uploaded

The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
Israel Genealogy Research Association
 
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental DesignDigital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
amberjdewit93
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
adhitya5119
 
Cognitive Development Adolescence Psychology
Cognitive Development Adolescence PsychologyCognitive Development Adolescence Psychology
Cognitive Development Adolescence Psychology
paigestewart1632
 
DRUGS AND ITS classification slide share
DRUGS AND ITS classification slide shareDRUGS AND ITS classification slide share
DRUGS AND ITS classification slide share
taiba qazi
 
MARY JANE WILSON, A “BOA MÃE” .
MARY JANE WILSON, A “BOA MÃE”           .MARY JANE WILSON, A “BOA MÃE”           .
MARY JANE WILSON, A “BOA MÃE” .
Colégio Santa Teresinha
 
The basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptxThe basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptx
heathfieldcps1
 
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHatAzure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Scholarhat
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
Katrina Pritchard
 
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments UnitDigital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit
chanes7
 
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingYour Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Excellence Foundation for South Sudan
 
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Academy of Science of South Africa
 
A Independência da América Espanhola LAPBOOK.pdf
A Independência da América Espanhola LAPBOOK.pdfA Independência da América Espanhola LAPBOOK.pdf
A Independência da América Espanhola LAPBOOK.pdf
Jean Carlos Nunes Paixão
 
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityNatural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Akanksha trivedi rama nursing college kanpur.
 
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxChapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Mohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
 
The History of Stoke Newington Street Names
The History of Stoke Newington Street NamesThe History of Stoke Newington Street Names
The History of Stoke Newington Street Names
History of Stoke Newington
 
Smart-Money for SMC traders good time and ICT
Smart-Money for SMC traders good time and ICTSmart-Money for SMC traders good time and ICT
Smart-Money for SMC traders good time and ICT
simonomuemu
 
How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17
Celine George
 
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdfLiberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
WaniBasim
 
S1-Introduction-Biopesticides in ICM.pptx
S1-Introduction-Biopesticides in ICM.pptxS1-Introduction-Biopesticides in ICM.pptx
S1-Introduction-Biopesticides in ICM.pptx
tarandeep35
 

Recently uploaded (20)

The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
 
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental DesignDigital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
 
Cognitive Development Adolescence Psychology
Cognitive Development Adolescence PsychologyCognitive Development Adolescence Psychology
Cognitive Development Adolescence Psychology
 
DRUGS AND ITS classification slide share
DRUGS AND ITS classification slide shareDRUGS AND ITS classification slide share
DRUGS AND ITS classification slide share
 
MARY JANE WILSON, A “BOA MÃE” .
MARY JANE WILSON, A “BOA MÃE”           .MARY JANE WILSON, A “BOA MÃE”           .
MARY JANE WILSON, A “BOA MÃE” .
 
The basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptxThe basics of sentences session 6pptx.pptx
The basics of sentences session 6pptx.pptx
 
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHatAzure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
 
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments UnitDigital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit
 
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingYour Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
 
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
 
A Independência da América Espanhola LAPBOOK.pdf
A Independência da América Espanhola LAPBOOK.pdfA Independência da América Espanhola LAPBOOK.pdf
A Independência da América Espanhola LAPBOOK.pdf
 
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityNatural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
 
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxChapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
 
The History of Stoke Newington Street Names
The History of Stoke Newington Street NamesThe History of Stoke Newington Street Names
The History of Stoke Newington Street Names
 
Smart-Money for SMC traders good time and ICT
Smart-Money for SMC traders good time and ICTSmart-Money for SMC traders good time and ICT
Smart-Money for SMC traders good time and ICT
 
How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17How to Fix the Import Error in the Odoo 17
How to Fix the Import Error in the Odoo 17
 
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdfLiberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
 
S1-Introduction-Biopesticides in ICM.pptx
S1-Introduction-Biopesticides in ICM.pptxS1-Introduction-Biopesticides in ICM.pptx
S1-Introduction-Biopesticides in ICM.pptx
 

Lesson 03 chapter 6 sampling

  • 1. Statistics 2 Dr. Ning DING IBS I.007 [email_address] You’d better use the full-screen mode to view this PPT file.
  • 2. Table of Contents Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 3. Sampling and Sampling Distribution Population = all items chosen for study Sample = a portion chosen from the population Parameter Statistic  Greek or capital letters  Lowercase Roman letters Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 4. Sampling and Sampling Distribution Population Sample Parameter Statistic N = number μ = mean σ = standard deviation n = number X = mean SD = standard deviation Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 5. Sampling Distribution Mean Mean Mean Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 6. Standard Error = Standard deviation of the distribution of a sample statistic Larger Standard Error Smaller Standard Error Which one is better? Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 7. Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 8. Standard Error Sample size Dispersion of sample means Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 9. Standard Error µ = 100 σ = 25 =95 =106 =101 Population Range=80~240 Sample Range=90~120 Standard Error of mean Standard Deviation of population ____ Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 10. Calculating the Standard Error Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test Sample means frequency
  • 11. Calculating the Standard Error individual savings accounts µ= $2000 σ = $600 Sample= 100 accounts the probability that the sample mean lies betw. $1900~$2050 ? Standard Error of the mean Population standard deviation Sample size Example: Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 12. Calculating the Standard Error the probability that the sample mean lies betw. $1900~$2050 ? Sample mean Population mean Standard error of the mean 0.4525 0.2967 + = 0.7492 74.92% of our sample means lies betw. $1900~$2050 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 13. Calculating the Standard Error 6-30 Chapter 6, No. 6-30 P.321 Known: Normal distribution, μ =375 σ =48 P=95% n = ? Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 14. Calculating the Standard Error 6-30 Chapter 6, No. 6-30 P.321 Known: Normal distribution, μ =375 σ =48 P=95% n=? Step 1: P=P z1 +P z2 =0.950 z 1 =-1.96 z 2 =1.96 370< <380 -1.96< z <1.96 Step 2: 1.96= Step 3: n=354.04 The sample size is at least 355 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 15. Calculating the Standard Error Infinite population Finite population Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 16. The Finite Population Multiplier population size sample size Finite population multiplier F.P.M. Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 17. The Finite Population Multiplier 1) N= 20 n=5 0.888 2) N= 20 n=19 0.229 3) N= 20 n=20 0 4) N= 1000 n=20 0.99 When to use F.P.M.? If Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 18. Calculating the Standard Error SC 6-7a Chapter 6, SC No. 6-7 P.327 Known: N=125 n=64 μ =105 σ =17 =? Step 1: n/N=64/125= 0.512 >0.05 Yes, it is allowed to use F.P.M. Step 2: = = = Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 19. Calculating the Standard Error SC 6-7b Chapter 6, SC No. 6-7 P.327 Known: N=125 n=64 μ =105 σ =17 =1.4904 P(107.5<Xmean<109) = ? Step 1: visualize and calculate z scores = = P 1 =0.4535 P 2 =0.4963 P=0.4963-0.4535=0.0428 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 20. Calculating the Standard Error SC 6-8 Chapter 6, SC No. 6-8 P.327 Known: n=36 μ =? σ =1.25 pounds What is the probability that the sample mean is within one-half pound of the population mean? = = Step 1: Visualize and calculate z scores Step 2: Calculate the standard error of sample means Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 21. Calculating the Standard Error SC 6-8 Chapter 6, SC No. 6-8 P.327 Known: n=36 μ =? σ =1.25 pounds What is the probability that the sample mean is within one-half pound of the population mean? = = Step 2: Calculate the standard error of sample means Step 3: Calculate the z scores P z1 =0.4918 P z2 =0.4918 + = 0.9836 Step 4: convert to P value Step 5: Finalize your answer Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 22. Chapter 7 Introduction of Estimation confidence level confidence interval Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 23. Types of Estimates Interval Estimates Point Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 24. Interval Estimates: Basic Concepts P.354 Interval Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months Stardard error of the mean from an infinite population standard deviation of the population sample size 36+0.707=36.707 36-0.707=35.293 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 25. Interval Estimates: Basic Concepts P.354 Interval Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =1.0 P=0.3413 68.3% of the actual mean lie between 35.293 and 36.707 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 26. Interval Estimates: Basic Concepts P.354 Interval Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =2.0 P=0.4775 95.5% of the actual mean lie between _______ and_________ 95.5% of the actual mean lie between 34.586 and 37.414 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 27. Interval Estimates: Basic Concepts P.354 Interval Estimates standard deviation is 10 interviewed 200 person according to them, the mean is 36 months z =3.0 P=0.4987 99.7% of the actual mean lie between 33.879 and 38.121 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 28. Interval Estimates: Basic Concepts P.354 Interval Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 29. Interval Estimates: Basic Concepts Interval Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test Interval =
  • 30. Interval Estimates: Basic Concepts Interval Estimates EX 7-27a Chapter 7, No. 7-27 P. 365 Known: n=40 EX 7-27b P=90%  z=1.645 Upper limit =1416+7.8029 Lower limit=1416-7.8029 =1424 =1408 90% confident that our population mean lies between 1408 and 1424. Interval Estimates Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test = Interval =
  • 31. Interval Estimates: Basic Concepts Interval Estimates If the σ is unknown , ? Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test Estimated standard error of mean Estimated standard error of proportion P.368 Interval = Interval = Interval =
  • 32. Interval Estimates: Basic Concepts Interval Estimates EX 7-35a EX 7-35b z=2.33 Answer: 0.01 ~0.09 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test Chapter 7, No. 7-35 P. 369 known: n=200 p=0.05 q=0.95 known: n=200 p=0.05 q=0.95 P=98% Interval = 
  • 33. Interval Estimates: Basic Concepts Interval Estimates If the sample size is =< 30, AND σ is unknown, ? t - distribution You can read the t value from Appendix Table 2 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 34. Interval Estimates: Basic Concepts Interval Estimates How to read the t-table ? t - distribution e.g. n=10  df= 9 P=90% 0.05 0.05 Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test
  • 35. Interval Estimates: Basic Concepts Interval Estimates How to use t value ? t - distribution Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates SPSS Tips for t-test interval = interval =
  • 36. Summary Chapter 6 Sampling - Review: Sampling and Standard Error - Calculating Standard Error-Infinite Population - Calculating Stanard Error-Finite Population Chapter 7 Introduction of Estimation - Types of Estimates - Interval Estimates
  • 37. The Normal Distribution SPSS Tip: t-test The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3
  • 38. The Normal Distribution SPSS Tip: t-test 3 types of t-test One Sample t-test Paired-Samples t-test Independent Samples t-test test whether the population mean is different from a constant test whether the population mean of differences between paired scores is equal to zero test the relationship between two categories and a quantitative variable
  • 39. The Normal Distribution SPSS Tip: t-test One Sample t-test Example: A researcher wants to evaluate whether customers believe price change is more a function of natural fluctuations in inflation or due to effects caused by human interventions. Thirty customers are assessed on the Price Change Attitude Scale , which yields scores that range from 0 ( due solely to natural fluctuations in inflation ) to 100 ( due solely to human interventions ). A score of 50 is the test value and represents an equal contribution of the two effects. The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3 One-Sample t-test.sav Variable Description PCAS Price Change Attitude Scale
  • 40. The Normal Distribution SPSS Tip: t-test One Sample t-test Example: A researcher wants to evaluate whether customers believe price change is more a function of natural fluctuations in inflation or due to effects caused by human interventions. Thirty customers are assessed on the Price Change Attitude Scale , which yields scores that range from 0 ( due solely to natural fluctuations in inflation ) to 100 ( due solely to human interventions ). A score of 50 is the test value and represents an equal contribution of the two effects. Null Hypothesis: The population mean is equal to 50. Variable Description PCAS Price Change Attitude Scale
  • 41. The Normal Distribution SPSS Tip: t-test Step 1: Choose Analyze--> Compare Means --> One-Sample T Test One Sample t-test
  • 42. The Normal Distribution SPSS Tip: t-test Step 2: Move the variable you want to test into the box ”Test Variable(s)”. Enter the value in the box “Test Value”. In this example, the PCAS middle value is 50. Click OK and you will see a popup window. One Sample t-test
  • 43. The Normal Distribution SPSS Tip: t-test One Sample t-test Read the next slide to know how to interpret it !
  • 44.
  • 45. The Normal Distribution SPSS Tip: t-test Example: A researcher is interested in determining whether customers’ satisfaction with DOVE body lotion improves when exposed to a new TV commercial. Thirty customers are assessed on the Satisfaction Scale for Customers (SSC) prior to and after the new TV commercial. The data can be downloaded from: Blackboard – Inductive Statsitics STA2—SPSS-- Week 3 Paired-Sample t-test.sav Paired-Samples t-test Variable Description Pre_SSC Percent correct on the Sales Scale for Customers prior to the new TV commercial Post_SSC Percent correct on the Sales Scale for Customers after the new TV commercial
  • 46. The Normal Distribution SPSS Tip: t-test Example: A researcher is interested in determining whether customers’ satisfaction with DOVE body lotion improves when exposed to a new TV commercial. Thirty customers are assessed on the Satisfaction Scale for Customers (SSC) prior to and after the new TV commercial. Paired-Samples t-test Null Hypothesis: The population means’ difference is zero. Variable Description Pre_SSC Percent correct on the Sales Scale for Customers prior to the new TV commercial Post_SSC Percent correct on the Sales Scale for Customers after the new TV commercial
  • 47. The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Step 1: Choose Analyze--> Compare Means --> Paired-Samples T Test
  • 48. The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Step 2: Move the first variable the box “Paired Variables”, to the location Variable 1, and the second variable to Variable 2. Click OK and you will see a popup window.
  • 49. The Normal Distribution SPSS Tip: t-test Paired-Samples t-test Read the next slide to know how to interpret it !
  • 50.