House Sales What proportion of the houses that sold for over $600,000 were on the market for less than 30 days? Days on the market Less than 30 days 30.

Slides:



Advertisements
Similar presentations
The Important Thing About By. The Important Thing About ******** The important thing about ***** is *****. It is true s/he can *****, *****, and *****.
Advertisements

Total Cost, Total Revenue, and Profit Change as You Sell More Shoes.
5 Elm Street Thurmont, MD Purchase: $123k Repairs & Carrying Costs: $27k Total Costs: $150k Sale Price $215k Profit: $65k Rehab completed in 14.
Appraisal Institute State of Atlanta Housing Market.
Random Thoughts 2012 (COMP 066) Jan-Michael Frahm Jared Heinly.
The ELISA HIV Blood Test Application example for probabilities ELISA is a test for AIDS used to screen donated blood for hiv. (See
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall. Section 11.1 Systems of Linear Equations; Substitution and Elimination.
3.4 “Solving Linear Systems with 3 Variables” The solutions are called an ordered triple (x,y,z). Equations are in the form of Ax + By + Cz = D. 1.4x +
YEAR 13 STATISTICS & MODELLING WORKSHOP DEPARTMENT OF STATISTICS House Sales What proportion of the houses that sold for over $600,000 were on the market.
1 Epidemiological Measures I Screening for Disease.
1 Year End 2012 Atlanta Housing Market Report. Single Family Detached Residences 4Q 2012 Quarterly Metro Market Report Provided By ChartMaster Services,
Percent Increase and Percent Decrease Problems. Percent Decrease A sofa regularly sells for $ Today it is on sale for $630. What is the percent.
Practical application. Population A ELISA test is applied to a million people where 1% are infected with HIV. Of the million people, 10,000 would be infected.
Section 11.1 Systems of Linear Equations; Substitution and Elimination.
Understanding CI for Means Ayona Chatterjee Math 2063 University of West Georgia.
240,000 20p + a SATMathVideos.Net A) 550 books B) 600 books C) 650 books D) 700 books 1,000 books sold in a month when the price was $10 per book. An equation.
The marketing mix Candidates should be able to: Define the marketing mix Assess the influences on the marketing mix such as finance, technology and market.
Section 12.3 Conditional Probability. Activity #1 Suppose five cards are drawn from a standard deck of playing cards without replacement. What is the.
Pie Sales. Start with a question. Are pie sales increasing? What day of the week has the biggest sales? What day of the week do they sell the least.
Intro to Bayesian Learning Exercise Solutions Ata Kaban The University of Birmingham.
10. General rules of probability
Errors in Hypothesis Tests
Chapter 3 Probability.
Unit 1: Statistics & Probability Lesson 3: Conditional Probability
Sampling Distributions and Estimators
Independent events Two events are independent if knowing that one event is true or has happened does not change the probability of the other event. “male”
REGIONAL REAL ESTATE REPORT – FEBRUARY 2017
Two Way Tables & Data Collection.
Class session 7 Screening, validity, reliability
Dharavi - True or False?.
a market for oranges overview: each round overview: transacting
Systems of Linear Equations; Substitution and Elimination
Bayesian Notions and False Positives
Online Gifts Buy for wishes happy mother's day to yours choice and with happy gifts find here:
We design, market and manage remarkable websites
Dates of … Quiz 5.4 – 5.5 Chapter 5 homework quiz Chapter 5 test
How do we judge efficacy of a screening test?
HIV Testing:.
Decision Theory.
3.10 Business and Economic Applications
The False Positive Paradox
Is a Positive Developmental-Behavioral Screening Score Sufficient to Justify Referral? A Review of Evidence and Theory  R. Christopher Sheldrick, PhD,
The Marketing Mix Promotion
Elementary Statistics: Picturing The World
4.4 – Applications; Exponential Models
Unit 6. Day 16..
מאפייני שוק העבודה של ערביי ישראל והמלצות מדיניות
Price Elasticity Using Coffee Example
In this meeting we will:
Section 3.3 Objectives Addition Rule
Both of these houses are listed for sale
Managing Difficult Conversations
Why I hate grading your ELA’s
Suppose I have ordered 140 Unities.
Section 3.3 Addition Rule Larson/Farber 4th ed.
Homework: pg ) P(A and B)=0.46*0.32= ) A B.) ) .3142; If A and B were independent, then the conditional probability of.
Section 4-2 Addition Rules
Getting Hired By Expireds Part 1
LI4 Inequalities- True or False?.
Standard 4: Understanding Investing
Finish Conditional Probability
Day 4 AGENDA: DG min.
1. Why Marketing Research?
Economics 100C: April 8, 2010 April 8, 2010.
Chapter 3 Probability.
Chapter 3 Probability.
Getting Hired By Expireds Part 1
Chapter 2, Unit E Screening Tests.
Competitive Price Lines as of _______________________________
Presentation transcript:

House Sales What proportion of the houses that sold for over $600,000 were on the market for less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

House Sales What proportion of the houses that sold for over $600,000 were on the market for less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

House Sales What proportion of the houses that sold for over $600,000 were on the market for less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

House Sales What is the probability a house sold for under $300,000 given that it sold in less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

House Sales What is the probability a house sold for under $300,000 given that it sold in less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

House Sales What is the probability a house sold for under $300,000 given that it sold in less than 30 days? Days on the market Less than 30 days 30 - 90 days More than 90 days Under $300,000 39 31 15 85 $300,000 - 600,000 35 45 4 84 Over $600,000 8 12 82 80 19 181 Selling price Total

Blood Group Systems (Rh & K) Rh system Kell system Outcome Probability 0.81 x 0.08 0.81 x 0.92 0.19 x 0.08 0.19 x 0.92 K+ K– Rh+ K+ Rh+ K– Rh– K+ Rh– K– 0.08 0.92 Rh+ 0.81 K+ K– 0.08 0.92 0.19 Rh–

Blood Group Systems (K & Rh) Kell system Rh system Outcome Probability 0.08 x 0.81 0.08 x 0.19 0.92 x 0.81 0.92 x 0.19 Rh+ Rh– K+ Rh+ 0. 81 0.19 K+ 0.08 K+ Rh– Rh+ Rh– 0.81 0.19 K– Rh+ 0.92 K– K– Rh–

ELISA: HIV Screening Test 98% true positive and 7% false positive Suppose 1% of the population have HIV Of those that test positive for HIV, what proportion have HIV? 98% of 0.01 Test result 7% of 0.99 Disease status Negative Positive Total Not HIV HIV 0.0098 0.0002 0.01 0.0693 0.9207 0.99 Total 0.0791 0.9209 1

ELISA: HIV Screening Test 98% true positive and 7% false positive Suppose 1% of the population have HIV. Of those that test positive for HIV, what proportion have HIV? Test result Disease status Negative Positive Total Not HIV HIV 0.0098 0.0002 0.01 0.0693 0.9207 0.99 Total 0.0791 0.9209 1

ELISA: HIV Screening Test 98% true positive and 7% false positive Suppose 1% of the population have HIV. Of those that test positive for HIV, what proportion have HIV? 0.0098 / 0.0791 = 0.1239 Test result Disease status Negative Positive Total Not HIV HIV 0.0098 0.0002 0.01 0.0693 0.9207 0.99 Total 0.0791 0.9209 1

ELISA: HIV Screening Test Proportion who don’t have HIV (99%) Proportion who have HIV (1%) Why is the probability of having HIV given that the test is positive so low?

ELISA: HIV Screening Test Positive tests Proportion who don’t have HIV (99%) 98% of 1% Proportion who have HIV (1%) Why is the probability of having HIV given that the test is positive so low?

ELISA: HIV Screening Test True False Positive tests Proportion who don’t have HIV (99%) 98% of 1% Proportion who have HIV (1%) 7% of 99% Why is the probability of having HIV given that the test is positive so low? Many more false positives than true positives

ELISA: HIV Screening Test Disease status Test result HIV and Pos HIV and Neg Not HIV and Pos Not HIV and Neg Outcome Probability 0.98 x 0.01 0.98 0.02 0.07 0.93 Pos | HIV Neg | HIV Pos | Not HIV Neg | Not HIV HIV Not HIV 0.01 0.99 0.02 x 0.01 0.07 x 0.99 0.93 x 0.99 Of those that test positive for HIV, what proportion have HIV? P(HIV Pos) = P(Pos | HIV) x P(HIV)

ELISA: HIV Screening Test Disease status Test result HIV and Pos HIV and Neg Not HIV and Pos Not HIV and Neg Outcome Probability 0.98 x 0.01 0.98 0.02 0.07 0.93 Pos | HIV Neg | HIV Pos | Not HIV Neg | Not HIV HIV Not HIV 0.01 0.99 0.02 x 0.01 0.07 x 0.99 0.93 x 0.99 Of those that test positive for HIV, what proportion have HIV?

ELISA: HIV Screening Test Disease status Test result HIV and Pos HIV and Neg Not HIV and Pos Not HIV and Neg Outcome Probability 0.98 x 0.01 0.98 0.02 0.07 0.93 Pos | HIV Neg | HIV Pos | Not HIV Neg | Not HIV HIV Not HIV 0.01 0.99 0.02 x 0.01 0.07 x 0.99 0.93 x 0.99 Of those that test positive for HIV, what proportion have HIV? 99 . 07 01 98 ) Pos | HIV ( P + ´ =