Senske’s First Block AP Statistics Alesha Seternus and Jenna Rorer.

Slides:



Advertisements
Similar presentations
Simple Probability and Odds
Advertisements

Four girls soccer teams took a random sample of players regarding the number of goals scored per game. The results are below. Use a significance level.
By Josh Spiezle, Emy Chinen, Emily Lopez, Reid Beloff.
For small degrees of freedom, the curve displays right-skewness.
CHAPTER 23: Two Categorical Variables: The Chi-Square Test
What is Probability? The study of probability helps us figure out the likelihood of something happening. In math we call this “something happening” or.
Chapter 10 Chi-Square Tests and the F- Distribution 1 Larson/Farber 4th ed.
By: John Marron Nicole Scamuffo
CHAPTER 11 Inference for Distributions of Categorical Data
Section 7.1 Hypothesis Testing: Hypothesis: Null Hypothesis (H 0 ): Alternative Hypothesis (H 1 ): a statistical analysis used to decide which of two competing.
11-2 Goodness-of-Fit In this section, we consider sample data consisting of observed frequency counts arranged in a single row or column (called a one-way.
Chi-square Goodness of Fit Test
Texting and Driving Joanna Curran And Brianna Baer.
Copyright © Cengage Learning. All rights reserved. 11 Applications of Chi-Square.
Means Tests Hypothesis Testing Assumptions Testing (Normality)
The table shows a random sample of 100 hikers and the area of hiking preferred. Are hiking area preference and gender independent? Hiking Preference Area.
Bell Work Suppose 10 buttons are placed in a bag (5 gray, 3 white, 2 black). Then one is drawn without looking. Refer to the ten buttons to find the probability.
13.1 Goodness of Fit Test AP Statistics. Chi-Square Distributions The chi-square distributions are a family of distributions that take on only positive.
Section 10.1 Goodness of Fit. Section 10.1 Objectives Use the chi-square distribution to test whether a frequency distribution fits a claimed distribution.
CONFIDENTIAL 1 Algebra1 Theoretical Probability. CONFIDENTIAL 2 Warm Up 1) choosing a heart. 2) choosing a heart or a diamond. An experiment consists.
Chapter 11: Applications of Chi-Square. Count or Frequency Data Many problems for which the data is categorized and the results shown by way of counts.
10.1: Multinomial Experiments Multinomial experiment A probability experiment consisting of a fixed number of trials in which there are more than two possible.
Dependent Probability. P(Yellow then Blue) = P (Pink then not blue) = P(Yellow then Yellow) = 3 32 == =
CHAPTER 20: Inference About a Population Proportion
Emily DeAngelis Megan Wolf AP Stat Final Project.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 11 Inference for Distributions of Categorical.
Chapter 11: Inference for Distributions of Categorical Data Section 11.1 Chi-Square Goodness-of-Fit Tests.
Chi-Square Test.
Chapter 10 Chi-Square Tests and the F-Distribution
GOODNESS OF FIT Larson/Farber 4th ed 1 Section 10.1.
1.4 Equally Likely Outcomes. The outcomes of a sample space are called equally likely if all of them have the same chance of occurrence. It is very difficult.
Chi-Square Test James A. Pershing, Ph.D. Indiana University.
List one thing that has a probability of 0?. agenda 1) notes on probability 2) lesson 1 example 1, 2 Exercise 5-8 Problem set 1-3 3)start lesson 3.
1 Chapter 10. Section 10.1 and 10.2 Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION.
Logic and Vocabulary of Hypothesis Tests Chapter 13.
Double Pack Tic Tacs Kamyia Mason Mariah Rosado. Introduction Question: Is there an equal amount of yellow and red tic tacs in a 1 oz. Cherry Passion.
AGENDA:. AP STAT Ch. 14.: X 2 Tests Goodness of Fit Homogeniety Independence EQ: What are expected values and how are they used to calculate Chi-Square?
PHANTOMS: A Method of Testing Hypotheses
Probability.
The table below gives the pretest and posttest scores on the MLA listening test in Spanish for 20 high school Spanish teachers who attended an intensive.
By: Avni Choksi and Brittany Nguyen
Lecture 11. The chi-square test for goodness of fit.
Inference for Tables Catapult Discovery Question: –How does a cat land (feet, side, nose/face)? –Write your predictions in percent. Collect data for.
By.  Are the proportions of colors of each M&M stated by the M&M company true proportions?
Color Distribution A block BrownYellowOrangeRedGreenBlue GreenYellowOrangeRedPurple M&M Skittles.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
+ Section 11.1 Chi-Square Goodness-of-Fit Tests. + Introduction In the previous chapter, we discussed inference procedures for comparing the proportion.
Hypothesis Testing Steps for the Rejection Region Method State H 1 and State H 0 State the Test Statistic and its sampling distribution (normal or t) Determine.
WARM – UP: The Math club and the Spanish club traditionally are composed of a similar distribution of class level. A random sample of this year’s math.
11.1 Chi-Square Tests for Goodness of Fit Objectives SWBAT: STATE appropriate hypotheses and COMPUTE expected counts for a chi- square test for goodness.
Hypothesis Tests Hypothesis Tests Large Sample 1- Proportion z-test.
Sampling Analysis. Statisticians collect information about specific groups through surveys. The entire group of objects or people that you want information.
Chapter 10 Chi-Square Tests and the F-Distribution.
Section 10.1 Goodness of Fit © 2012 Pearson Education, Inc. All rights reserved. 1 of 91.
Chi Square Test of Homogeneity. Are the different types of M&M’s distributed the same across the different colors? PlainPeanutPeanut Butter Crispy Brown7447.
Bell Work.
Statistics Collecting and analyzing large amounts of numerical data
Section 10-1 – Goodness of Fit
Elementary Statistics: Picturing The World
Chi-Square Test.
Chi-Square Test.
Chi-Square - Goodness of Fit
Goodness of Fit Test - Chi-Squared Distribution
X2 = Based on the following results, is the die in
Chi-Square Test.
Chi-squared tests Goodness of fit: Does the actual frequency distribution of some data agree with an assumption? Test of Independence: Are two characteristics.
Sample Mean Compared to a Given Population Mean
Sample Mean Compared to a Given Population Mean
Chapter 26 Part 2 Comparing Counts.
Presentation transcript:

Senske’s First Block AP Statistics Alesha Seternus and Jenna Rorer

Objective: Be the first player to reach the Candy Castle by landing on the multi-colored rainbow space at the end of the path.

Sixty-four cards in a deck: 36 single-colored cards 22 double-colored cards 6 character cards The card colors consist of red, orange, yellow, green, blue, and purple The characters are Grandma Nutt, Mr. Mint, Jolly, Gingerbread, Lolly, and Princess Frostine

We have chosen the Chi-Squared Test in order to examine the following probabilities… Test One: The Probability of Choosing a Single- Colored Card from the Deck Test Two: The Probability of Choosing a Double- Colored Card from the Deck Test Three: The Probability of Choosing a Character Card from the Deck

Test One: The Probability of Choosing a Single Colored Card from the Deck Hypothesis: Ho:The observed frequency distribution for picking a single colored card fits the specified distribution Ha: The observed frequency distribution for picking a single colored card does not fit the specified distribution Assumptions: State: SRS Sample size large enough that all expected values are greater than or equal to 5 Check: Assumed Refer to chart

Trial RedPurpleYellowBlueOrangeGreen

TrialExpected Value Observed Value

Test One:The Probability of Choosing a Single-Colored Card from the Deck   obs-exp) 2 = exp ( ) 2 + ( ) = p(> ) = Conclusion: We fail to reject Ho in favor of Ha because our P-value is greater than alpha (0.05). We have sufficient evidence that the observed frequency distribution for picking a single colored card fits the specified distribution. df (k-1) = 29

Test Two: The Probability of Choosing a Double- Colored Card from the Deck Hypothesis: Ho:The observed frequency distribution for picking a single colored card fits the specified distribution Ha: The observed frequency distribution for picking a single colored card does not fit the specified distribution Assumptions: State: SRS Sample size large enough that all expected values are greater than or equal to 5 Check: Assumed Refer to chart

RedPurpleYellowBlueOrangeGreen

TrialExpected Value Observed Value

Test Two: The Probability of Choosing a Double- Colored Card from the Deck   (obs-exp) 2 = ( )2 + ( )2 + … exp = p( > ) = Conclusion: We fail to reject Ho in favor of Ha because our P-value is greater than alpha (0.05). We have sufficient evidence that the observed frequency distribution for picking a single colored card fits the specified distribution df (k-1) = 29

Test Three: The Probability of Choosing a Character Card from the Deck Hypothesis: Ho:The observed frequency distribution for picking a character card fits the specified distribution Ha: The observed frequency distribution for picking a character card does not fit the specified distribution Assumptions: State: SRS Sample size large enough that all expected values are greater than or equal to 5 Check: Assumed Refer to chart

Trial Gingerbr ead Mr. Mint JollyPrincess Frostine Gramma Nutt Lolly

TrialExpected Value Observed Value

Test Three: The Probability of Choosing a Character Card from the Deck =  (obs-exp) 2 = ( ) 2 + ( ) 2 + … exp p( > ) = = Conclusion: We fail to reject Ho in favor Ha because our P-value is greater than alpha (0.05). We have sufficient evidence that the observed frequency distribution for picking a character card fits the specified distribution. df (k-1) = 29

Personal Opinions/ Conclusions Bias/Error Our experiment was conducted through random samplings of the 64 cards (no bias) An example of a bias experiment would be if we had arranged or drawn the cards in a specific order or pattern as to predict/control the outcomes. If the 30 trials happened to be played by separate groups, all groups had to collect data under identical conditions. We have come to the conclusion that the probabilities of picking either a single-colored, double-colored, or character card is similar to the expected values.