Chapter 5 Homework
Due: Feb 26, 2017
Case Study: Planning a Heart-Healthy Diet
Jackie is a 21-year-old health-conscious individual majoring in business. She recently learned that a diet
high in satura
Chapter 2
Organizing and Summarizing Data
2.1 Organizing Qualitative Data
1. Raw data are the data as originally collected, before they have been organized or coded.
2. number (or count); proportion (
Chapter 11
Inferences on Two Samples
11.1 Inferences about Two Means: Dependent Samples
1. independent
2.
dependent
3. Since the researcher claims the mean of population 1, 1 , is less than the mean o
Chapter 1
Data Collection
1.1 Introduction to the Practice of Statistics
1. Statistics is the science of collecting, organizing, summarizing and analyzing information
in order to answer questions or d
Chapter 5
Probability
5.1 Probability Rules
1. Empirical probability is based on the outcome of a probability experiment and is
approximately equal to the relative frequency of the event. Classical pr
4-8 Exponential Distribution
f(x)
x
2
4-8 Exponential Distribution
The exponential distribution is sometimes used to model the waiting
time to an event. It turns out that the exponential distribution
1
2
4
5
6
Example
Example
4-7 Normal Approximation to the
Binomial and Poisson Distributions
The normal distribution can be used to approximate the binomial
distribution when n is large.
For large va
Continuous Random Variable:
is a random variable with an interval ( either
finite or infinite) of real numbers for its range
Examples: length, electric current, time, temperature,.
The probability dis
Chapter 3
Numerically Describing Data from One Variable
3.1 Measures of Central Tendency
1. A statistic is resistant if it is not sensitive to extreme data values. The median is resistant
because it i
3-9 Poisson Distribution
A widely used discrete probability distribution; Consider the following conditions:
- p is very small and approaches 0
example: a 100 sided dice instead of a 6 sided dice, p =
2-6 Independence
Definition (two events)
Also,
Example 2-29
What is P(B)?
Does P(B|A) = P(B)?
2
Example 2-30 Text
D denotes the event that a part is defective
F denotes the event that a part has a sur
3-1 Random Variables
- Random (Stochastic) experiment is defined as that whose
outcome cannot be predicted for sure
- The occurrence of a specific outcome of a stochastic process
( or an experiment) i
4-5 Continuous Uniform Random Variable
Definition
4-5 Continuous Uniform Random Variable
Mean and Variance
4-5 Continuous Uniform
Random Variable
4-5 Continuous Uniform Random Variable
4-6 Normal Dist
Conditional Probability Distributions
Two Discrete Random Variables
When two random variables are defined in a random experiment,
knowledge of one can change the probabilities that we associate
with t
2-1 Sample Spaces and Events
2-1.1 Random Experiments
Controlled Variables
Input
Text
System
Output
Uncontrolled Variables
(noise)
Because of the uncontrollable inputs, the
same settings for the contr
2-2 Interpretations of Probability
2-2.1 Introduction
Probability
Used to quantify likelihood or chance
Repeat an Experiment many times, assume that each trial of the experiment results
in only one o
Independence:Two Discrete Random Variables
Joint and marginal probability
distributions of X and Y
Conditional probability distribution
of Y given X=x
Independence:Two Continuous Random Variables
3
Ex
Joint Probability Distribution,
2
Example: (Jointly discrete RV),
consider
The joint probability mass function is the function f XY(x,y)=P(X=x,
Y=y), for example, fXY(129,15)= 0.12
3
4
5
6
Example:
8
Chapter 4
Describing the Relation between Two Variables
4.1 Scatter Diagrams and Correlation
1. Univariate data measures the value of a single variable for each individual in the study.
Bivariate data
Chapter 7
The Normal Probability Distribution
7.1 Properties of the Normal Distribution
1. For the graph to be that of a probability density function,
(1) The area under the graph over all possible va
Chapter 8
Sampling Distributions
8.1 Distribution of the Sample Mean
1. The sampling distribution of a statistic (such as the sample mean) is the probability
distribution for all possible values of th
Chapter 9
Estimating the Value of a Parameter Using Confidence Intervals
9.1 The Logic in Constructing Confidence Intervals about a Population
Mean Where the Population Standard Deviation is Known
1.
Chapter 10
Testing Claims Regarding a Parameter
10.1 The Language of Hypothesis Testing
1. A Type I error is the error of rejecting H 0 when in fact H 0 is true. A Type II error is the
error of not re
Chapter 12
Inference on Categorical Data
12.1 Goodness of Fit Test
1. These procedures are for testing whether sample data are a good fit with a hypothesized
distribution.
2. The 2 goodness of fit tes
American University of Afghanistan (AUAF)
Department of Math and Science
Statistics -1
Summer Semester 2015
Instructor: Asadullah Jawid (M. Sc. University of Bonn)
Email: [email protected]
Office: Fa
ASSIGNMENT SOLVED
4. Why is a 99% confidence interval wider than a 95% confidence interval?
Answer. A 95% Confidence Interval would be narrower than a 99% Confidence Interval. This
occurs because the
1
- Often in practice we are interested in drawing valid conclusions about a large group
of individuals or objects.
- Instead of examining the entire group, called the population, which may be
difficu