STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Know how to make a Box plot and a modified Box plot
Know how to make no
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Calculate sample proportion, mean and standard deviation of a sample
d
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Understand the concept of associations
Know how to carryout design of e
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Know how to find the correlation between two variables
Know how to perf
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Distinguish the standard deviation of a the sample mean from the
stand
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Formulate the null and alternative hypotheses of a significant test
De
STATS 240: BASIC STATISTICS
Andrew Agbonigha O.I UNBC
Outline
1. Learning Objective
2. Activities and MACE Hour Schedule
Learning Objective
By the end of this Lab you will be able to:
Use probability distribution to find the mean of a discrete random
vari
STAT 240: Midterm 2 Coverage and Practice Exercises
_
NOT Open Book exam.
Formula sheet/Statistical tables will be given.
Chapter 4 only.
Calculator (any type) allowed.
No part marks for a wrong answer.
_
Practice problems
Textbook 7th Ed.
Chapter 4
(4.2)
3.2 Design of Experiments
Individuals upon which experiments are done
are experimental units if inanimate,
subjects if animals, and participants if
human
A specific experimental condition applied to
the units is a treatment
Kevin J. Keen 2016
1
Example
2.3 Correlation
We say a linear relationship is strong if points
lie close to a straight line and weak if they are
widely scattered about
However, our eyes are not a good measure of
strong a relationship
We rely on a statistic called the correlation
to
2.5 Cautions about Correlation and
Regression
We need a firm grasp of the use and
limitations of correlation and regression
Kevin J. Keen 2016
1
Residuals
A residual is the difference between an observed
value and the value predicted by the regression
Chapter 3
Producing Data
Exploratory
Data Analysis
Is what we have been doing
Asks us to visualize the data
Formal
Statistical Inference
Is what we shall start to do
Tells what inferences can be drawn from the
data for a known degree of confidence
3.1 Sou
3.3 Sampling Design
The entire group of individuals that we want
information about is the population
A sample is the subset of the population that
is actually examined
Kevin J. Keen 2016
1
A voluntary response sample consists of
people who choose them
2.6 Data Analysis for Two-Way
Tables
Statistical analysis of two-way tables is
likely the most widely used method of the
subfield of statistics referred to as
categorical data analysis
We begin our discussion with a motivating
example
Kevin J. Keen 201
Chapter 5
Sampling Distributions
Statistical inference makes conclusions
about a population based upon
Statistics observed on a sample
A probability model for the sample
Kevin J. Keen 2016
The Population Distribution
The population distribution of a v
2.4 Least-Square Regression
We summarize the overall linear pattern
between two variables by drawing a
regression line on the scatterplot
Kevin J. Keen 2016
1
Regression Line
A regression line is a straight line that
describes how a response variable y
2.3: R and R2
2.4: Regression line y hat=a+bx,
b=r(sy/sx),
a = mean of y b(mean of x)
2.5: Residual = observed y predicted y residual = y y hat
2.6: Simpsons Paradox: The association that holds for each of the two groups reverses direction when
the data a
2.7 The Question of Causation
Can you give examples of pairs of variables
for which causation has been
Accepted?
Rejected?
Can you state why this decision has been
made?
Kevin J. Keen 2016
1
Explaining Association
Causation: a change in x causes a c
Part 3 Groundwater Flow to Wells
1
Steady Unidirectional Flow in Confined Aquifer
Head h
h1
b
q
h2
Example 30: If h1 = 120 ft, h2 =
110 ft, L = 2000 ft, b =230 ft, K
= 0.25 ft/hr, what is the flow
rate per unit width (q)?
q' =
Q
KAi K (b width)i
dh
=
=
=
Ch. 3. Discrete Random variables &
Their Probability Distributions
Why to study the probability distribution
The probability of an observed sample is
needed to make inferences about a
population.
The sample observations are frequently
expressed as numeric
Ch. 4. Continuous Random variables &
Their Probability Distributions
The type of r.v. that takes on any value in
an interval is called continuous.
The p.d. for a continuous r.v., unlike the
p.d. for a discrete r.v., can not be obtained
by assigning nonzer
Using Graphs & Tables to
Describe Data
Chapter 2
1
Using Graphs & Tables to Describe
Data
2.1 Types of Data
2.2 Frequency Distributions & Histograms
for Quantitative Data
2.3 Tables, Bar Graphs & Pie Charts for
Qualitative Data
2.4 Time-Series Graphs
2.5
Using Numbers to Describe Data
Chapter 3
1
Using Numbers to Describe Data
3.1 Some Useful Notation
3.2 Measures of Central Tendency
3.3 Measures of Variability
3.4 Measures of Association
Chapter 3
2
Some Useful Notation
Statistics involves arithmetic
M
2. Probability
Probability
To provide a model for the repetition of an experiment
To provide a model for the population frequency
distributions
Definitions and Concepts
Probability: The measure of ones belief in the
occurrence of a future event.
Random or
STAT 371 Probability and Statistics for Scientists and Engineers
Assignment 2 [30]
Submit by: Wednesday, October 12, 2016
[10] Problem 1. The number of imperfections in a weave of textile has a Poisson distribution
with a mean of 4 per square-yard.
a) Fin
1. Introduction
2
Groundwater Hydrology: What is it?
Hydrology: study of the occurrence, distribution, movement
and properties of the waters of the earth and their
environmental relationship
including surface water
hydrology and groundwater hydrology
Grou
Calculating Probabilities
Chapter 4
1
Calculating Probabilities
4.1 Sample Spaces & Basic Probabilities
4.2 Conditional Probabilities & the Test for
Independence
4.3 And, Or & Not Probabilities
Chapter 4
2
Introduction
Probability analysis allows us to e
Review
for Test #1
Chapter 1 Looking at Data
Sections 1.1, 1.2, 1.3
Chapter 2 Looking at Data
Relationships
Sections 2.1 to 2.2 only
Section 1.1
Displaying Distributions
Variables
Categorical
Quantitative
Barplots
Stem-and-Leaf
Histograms
Plots (Stemplots
S”
SJ‘
7.
8.
9.
10.
ll.
STAT 240 Basic Statistics
Term Test 2
Name:
INSTRUCTIONS
. The total number of marks possible is 30.
Write your name on each page in the space provided.
Because of the 50 minute duration of this test, you are not permit