STAT 200 Chapter 5 Sampling Distributions
The binomial distribution for counts (Section 5.1)
1. Motivating examples:
(a) You ip a coin 10 times. How many times will a head turn up?
(b) One hundred Vancouver residents are randomly chosen and asked if they
Exercise Questions: Chapter 7
7.18 One-sided versus two-sided P-values. Computer software reports = 15.3 and P =
0.04 for a t test of H0: = 0 versus Ha: 0. Based on prior knowledge, you can justify
testing the alternative Ha: > 0. What is the P-value for
Exercise Questions: Chapter 4
4.1 Use Table B. We can use the random digits in Table B in the back of the text to
simulate tossing a fair coin. Start at line 109 and read the numbers from left to right. If
the number is 0, 1, 2, 3, or 4, you will say that
Labs 4 & 5: Sampling Distributions
Pre-Reading
STAT 200
Objectives
Generate random numbers using Rcmdr and an applet.
Produce a data set that represents a sample from a specific
distribution (e.g. Normal or Uniform) and calculate summary statistics.
Pr
STAT 200 Chapter 6 Introduction to Inferences
Basic concepts: estimation, estimators and estimates
1. Estimators vs. Estimates:
An estimator is a function of random variables X1 , X2 , Xn in a random sample
of size n) while an estimate is the realized val
STAT 200 Chapter 4 Probability: The Study of Randomness
Randomness (Section 4.1)
Consider throwing a thumbtack. The outcome can be a point-up or point-down, but
we cannot predict with certainty which outcome will occur before throwing the thumbtack.
This
STAT 200 Chapter 3 Producing Data
Data arise in everyday life. Some examples are:
A market research company tele-surveyed 963 individuals and asked about their banking preference and behaviour. Demographic information including age, gender, income
level
1. Indicate which section you are in:
(a) 201 (9am class)
(b) 202 (2pm class)
(c) Deferred student from a previous term.
2. All human blood can be typed as one of A, O, B, or AB. Suppose that,
in a very large population, 50% of people are type O, 20% are
CHAPTER 6 WELCOME TO
INFERENTIAL STATISTICS
STAT 200
Outline
2
Estimation
Point Estimation
Confidence Intervals
Hypothesis Testing
The Z-test
Types of Error
P-value
STAT 200 - Introduction to Statistics
In Reality
3
In the last chapter we explored the sam
Exercise Questions: Chapter 5
5.9 What is wrong? Explain what is wrong in each of the following scenarios.
(a) If you toss a fair coin three times and a head appears each time, then the next toss is
more likely to be a tail than a head.
(b) If you toss a
Exercise Questions: Chapter 6
6.11 Changing the sample size. Suppose that the sample mean is 50 and the standard
deviation is assumed to be 5. Make a diagram similar to Figure 6.5 (page 362) that
illustrates the effect of sample size on the width of a 95%
CHAPTER 7 MORE
CONFIDENCE INTERVALS AND
HYPOTHESIS TESTS
STAT 200
The Big Picture of Chapter 7
2
Up to now weve seen the Z-test and the confidence
intervals for means.
Both methods required three conditions:
Independent
observations
Normally distribute
Exercise Questions: Chapter 4
4.49 Discrete or continuous. In each of the following situations decide if the random
variable is discrete or continuous and give a reason for your answer.
(a) Your Web page has five different links and a user can click on on
Exercise Questions: Chapter 3
3.7
Cell phones and brain cancer. One study of cell phones and the risk of brain cancer
looked at a group of 469 people who have brain cancer. The investigators matched each
cancer patient with a person of the same sex, age,
Exercise Questions: Chapter 1
1.10 Survey of students. A survey of students in an introductory statistics class asked the
following questions: (a) age; (b) do you like to dance? (yes, no); (c) can you play a musical
instrument (not at all, a little, prett
CONTINGENCY TABLES
STAT 200
Outline
2
Contingency Tables: Displaying bivariate
categorical data.
The
different distributions
The Chi-Square Test for independence
Simpsons paradox
Objectives
3
Know how to run a Chi-Squared test from start to
finish (Stati
CHAPTER 5 THE CENTRAL
LIMIT THEOREM
STAT 200
Outline
2
This Chapter is in two distinct parts
In the first part we look at a named discrete
distribution:
The Binomial Distribution
In the second part we look at the fundamental theorem of
Statistics: The Cen
STAT 200 Chapter 1 Looking at Data - Distributions
What is Statistics?
Statistics is a science that involves the design of studies, data collection, summarizing and
analyzing the data, interpreting the results and drawing conclusions.
Inferences (conclusi
STAT 200 Chapter 7 Inference for Distributions
Inferences based on the t-distribution (Section 7.1)
Let X be a random variable that follows a certain distribution with mean and
standard deviation . If we have a large enough sample size n (> 30), and othe
Lab 2: Descriptive Statistics
Pre-Reading
Objectives
Be able to use Rcmdr to
Calculate measures of center and spread
Create a histogram
Create a boxplot
Obtain five-number summary
Understand the effect of outliers and distribution shape on the measu
Lab 3: Regression
Pre-Reading
STAT 200
In lab 3, we will investigate the relationship between two variables using linear
regression and use models to make predictions.
Objectives
Perform a regression analysis.
Understand which variable is the response and
Lab 8: Testing Hypotheses About Proportions
Pre-Reading
In statistics, hypothesis testing is a technique commonly used to assess if data show enough
evidence to support a given statement. In this pre-reading, we give a brief summary about
the steps when p
Concepts on Correlation
Correlation refers to the degree of linear association between two quantitative
variables x and y.
Positive correlation: large values of xs are linearly associated with large values of
ys
Negative correlation: large values of xs
Lab 7: Condence Interval of Proportion
Pre-Reading
1
Overview
In Lab 6, we have seen that an unknown parameter can be estimated by
a single value that summarizes a relevant data set. If p and p respectively
represent the population proportion and the samp
STAT 200 Chapter 10 Inference for Regression
Linear Regression (Section 10.1)
Recall in Chapter 2, we explored the relationship between two quantitative variables
by examining the scatterplot, measuring the strength of a linear association with the
corre
m Take Tm: Qu X
+ (D n https:f/cohnect.ubeca/inrebaops/asses5meht/take/launchyspkourseassessmehtiid:il424297l Etcourseiida C Q Search * I E u ' 9 a E
MostVisiled U Getting Started I Adele PerimmsAiiiAm El divergent parabola Iiiii Chemi2i i99
Lecture notes, Lang Wu, UBC
1
Chapter 8. Inference for Population Means
and the t-tests
When working with continuous populations, researchers often use statistical inference to draw conclusions about one or two population means, since means are the most
i
Lecture notes, Lang Wu, UBC
1
Chapter 9. Analysis of Variance
In this chapter, we again consider continuous populations. The two-sample t-test
in the previous chapter is widely used to compare two population mean parameters.
In practice, we may also wish
Lecture notes, Lang Wu, UBC
1
Chapter 7. Inference for Population Proportions
7.1. Introduction
In the previous chapter, we have discussed the basic ideas of statistical inference. To
illustrate the basic ideas, we considered confidence intervals and hypo
Inference for population means
Population: continuous, with unknown parameters mean
and standard deviation (SD) s . Example: the population is
normal N(, s ).
Objective: make inference for the mean based on data/sample,
i.e., constructing confidence inte