Central Limit Theorem For a sample size n  30 taken from a non- normal population with mean  and variance  2 :

Slides:



Advertisements
Similar presentations
 I consistently calculate confidence intervals and test statistics correctly, showing formula, substitutions, correct critical values, and correct margins.
Advertisements

Week11 Parameter, Statistic and Random Samples A parameter is a number that describes the population. It is a fixed number, but in practice we do not know.
Inference Sampling distributions Hypothesis testing.
Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 9.1 Chapter 9 Sampling Distributions.
A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true
Inferential Statistics & Hypothesis Testing
Probability & Statistical Inference Lecture 6
EPIDEMIOLOGY AND BIOSTATISTICS DEPT Esimating Population Value with Hypothesis Testing.
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 9: Hypothesis Tests for Means: One Sample.
BHS Methods in Behavioral Sciences I
Chapter 9 Hypothesis Testing.
1 BA 555 Practical Business Analysis Review of Statistics Confidence Interval Estimation Hypothesis Testing Linear Regression Analysis Introduction Case.
Clt1 CENTRAL LIMIT THEOREM  specifies a theoretical distribution  formulated by the selection of all possible random samples of a fixed size n  a sample.
Standard error of estimate & Confidence interval.
Statistical Inference for Two Samples
Sample Of size 2 Of size 3 1 A,B=3,1 2 A,B,C=3,1,5 3 A,C=3,5 4
Experimental Statistics - week 2
An importer of Herbs and Spices claims that average weight of packets of Saffron is 20 grams. However packets are actually filled to an average weight,
Section 9.1 Introduction to Statistical Tests 9.1 / 1 Hypothesis testing is used to make decisions concerning the value of a parameter.
Review of Statistical Inference Prepared by Vera Tabakova, East Carolina University ECON 4550 Econometrics Memorial University of Newfoundland.
1 BA 275 Quantitative Business Methods Hypothesis Testing Elements of a Test Concept behind a Test Examples Agenda.
Review of Basic Statistics. Definitions Population - The set of all items of interest in a statistical problem e.g. - Houses in Sacramento Parameter -
STAT 5372: Experimental Statistics Wayne Woodward Office: Office: 143 Heroy Phone: Phone: (214) URL: URL: faculty.smu.edu/waynew.
Binomial and Related Distributions 學生 : 黃柏舜 學號 : 授課老師 : 蔡章仁.
Fundamentals of Data Analysis Lecture 4 Testing of statistical hypotheses.
Topic 5 Statistical inference: point and interval estimate
Introduction to Probability and Statistics Chapter 8 Large-Sample Estimation.
Statistical inference. Distribution of the sample mean Take a random sample of n independent observations from a population. Calculate the mean of these.
-Test for one and two means -Test for one and two proportions
1 rules of engagement no computer or no power → no lesson no SPSS → no lesson no homework done → no lesson GE 5 Tutorial 5.
Estimation This is our introduction to the field of inferential statistics. We already know why we want to study samples instead of entire populations,
Statistical Hypotheses & Hypothesis Testing. Statistical Hypotheses There are two types of statistical hypotheses. Null Hypothesis The null hypothesis,
SAMPLING DISTRIBUTIONS & CONFIDENCE INTERVAL
Section 6-5 The Central Limit Theorem. THE CENTRAL LIMIT THEOREM Given: 1.The random variable x has a distribution (which may or may not be normal) with.
Determining the Size of a Sample 1 Copyright © 2014 Pearson Education, Inc.
Statistical Inference Statistical Inference involves estimating a population parameter (mean) from a sample that is taken from the population. Inference.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 8 Hypothesis Testing.
Physics 270 – Experimental Physics. Let say we are given a functional relationship between several measured variables Q(x, y, …) x ±  x and x ±  y What.
1 Objective Compare of two population variances using two samples from each population. Hypothesis Tests and Confidence Intervals of two variances use.
Hypothesis Testing for Standard Deviation or Variance.
Machine Learning Chapter 5. Evaluating Hypotheses
1 BA 275 Quantitative Business Methods Quiz #2 Sampling Distribution of a Statistic Statistical Inference: Confidence Interval Estimation Introduction.
MeanVariance Sample Population Size n N IME 301. b = is a random value = is probability means For example: IME 301 Also: For example means Then from standard.
Chapter5: Evaluating Hypothesis. 개요 개요 Evaluating the accuracy of hypotheses is fundamental to ML. - to decide whether to use this hypothesis - integral.
Hypothesis Testing Errors. Hypothesis Testing Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean.
Example Random samples of size n =2 are drawn from a finite population that consists of the numbers 2, 4, 6 and 8 without replacement. a-) Calculate the.
An importer of Herbs and Spices claims that average weight of packets of Saffron is 20 grams. However packets are actually filled to an average weight,
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
C HAPTER 4  Hypothesis Testing -Test for one and two means -Test for one and two proportions.
Understanding Basic Statistics Fourth Edition By Brase and Brase Prepared by: Lynn Smith Gloucester County College Chapter Nine Hypothesis Testing.
Section 6.4 Inferences for Variances. Chi-square probability densities.
1 Probability and Statistics Confidence Intervals.
Monday, October 21 Hypothesis testing using the normal Z-distribution. Student’s t distribution. Confidence intervals.
Elementary Probability.  Definition  Three Types of Probability  Set operations and Venn Diagrams  Mutually Exclusive, Independent and Dependent Events.
Hypothesis Testing  Test for one and two means  Test for one and two proportions.
Standard Deviation Normal Distribution. A common type of distribution where more values are closer to the mean, and as you move away from the mean, there.
Hypothesis Testing. Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean μ = 120 and variance σ.
Statistical Inference for the Mean Objectives: (Chapter 8&9, DeCoursey) -To understand the terms variance and standard error of a sample mean, Null Hypothesis,
CIVE Engineering Mathematics 2.2 (20 credits) Statistics and Probability Lecture 6 Confidence intervals Confidence intervals for the sample mean.
Example Random samples of size n =2 are drawn from a finite population that consists of the numbers 2, 4, 6 and 8 without replacement. a-) Calculate the.
And distribution of sample means
Ch5.4 Central Limit Theorem
Properties of Normal Distributions
Chapter 2 Hypothesis Testing Test for one and two means
Chapter 9 Hypothesis Testing
Introduction to Probability and Statistics
Chapter Nine Part 1 (Sections 9.1 & 9.2) Hypothesis Testing
Chapter 9 Hypothesis Testing
Testing and Estimating a Single Variance or Standard Deviation
How Confident Are You?.
Presentation transcript:

Central Limit Theorem For a sample size n  30 taken from a non- normal population with mean  and variance  2 :

Significance testing A pre-specified value to compare against a probability is called the significance level and is usually quoted as a percentage e.g. we may say something is significant at the 5% level.

Example 1 Electric light bulbs have lifetimes that are normally distributed with mean 1200 hours and standard deviation 150. It is suspected that a batch is substandard. To test this a sample of 50 bulbs is taken. a)Determine the significance level of a rule which would conclude that a batch is substandard if the sample mean lifetime is less than 1160hrs.

Example 1 (b) Determine the rule which would have a significance level of 1%. (c) What conclusion should be drawn if the observed sample mean is 1150hrs and the chosen significance level is 1%?

Example 2 A machine filling milk cartons delivers mean amount of 500ml per carton, variance 70.3ml. A sample of 50 cartons taken to check the machine has a mean amount of milk of 502.5ml. Test, using a 5% significance level, whether the machine needs adjusting.

Example 3 A garden centre sells flower seeds that have a germination rate of A packet of 20 seeds were sown. A new brand of seeds claims to have a higher germination rate. a) Find the significance level of the rule that accepts the claim if X  18. b) Find a rule whose significance level is c) What conclusion should be drawn if 19 seeds germinate?

Example 4 A coin is tossed 50 times to test if it's unbiased. a) Find the significance level of the rule that the coin is biased if X  19 or X  31 where X is the number of heads. b) Using a 5% significance level what conclusion should be drawn if 32 heads are obtained?

Example 5 A traffic warden issues a mean number of 1.6 tickets per day. The Council decides to employ another to see if more tickets will be issued. They work 5 days. a) The council decides that if the number of tickets was 12 or more they would conclude more tickets are issued. Find the significance level of this rule. b) Given that the observed value was 13 find the p-value and deduce the minimum significance level for the conclusion to be that the mean number of tickets issued per weekday has increased.

Example 6 Lightning strikes a church at a rate of 0.05 times per week. In a ten year period the church was struck 35 times. The vicar claims there has been an increase in electrical storms. Test at a 5% significance level if he is correct.

Confidence Intervals If I want the values (i.e. the interval) that, say, 95% of my data lies between I can use the Normal Distribution.

In general A Confidence Interval (CI) is found by: The critical value depends on the %:

Example 1 The pulse rate of 90 people was taken. The mean was 70 beats per minute and the standard deviation was 5 beats. Find a)A 95% CI for the population mean b)A 98% CI c)99% CI d)90% CI e)94% CI

Example 2 A plant produces steel sheets whose weights have a Normal Distribution with sd 2.1kg. A random sample of 49 sheets had mean weight 36.4kg. Find a 99% CI for the population mean.

Confidence Intervals for the difference between two means In general, The larger the sample the more accurate the CI. If they’re not Normal Variables but N is large we use the Central Limit Theorem.

Example