The linear probability model‚ ctd. When Y is binary‚ the linear regression model Yi = β0 + β1Xi + ui is called the linear probability model. • The predicted value is a probability: • E(Y|X=x) = Pr(Y=1|X=x) = prob. that Y = 1 given x • Yˆ = the predicted probability that Yi = 1‚ given X • β1 = change in probability that Y = 1 for a given ∆x: Pr(Y = 1 | X = x + ∆x ) − Pr(Y = 1 | X = x ) β1 = ∆x 5 Example: linear probability model‚ HMDA data Mortgage denial v. ratio of debt payments to income (P/I
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s Name (Print): Student ID No.: Session Number: _______________________ The following question will appear on your final exam. If you mark the box with either a or ‚ your midterm score will not be used in grade calculation. If the box is left blank‚ midterm score will be counted. EXAM Rules: This is an open-book‚ open-notes exam. Please leave your cell phone in your locker during the final exam on 10/10 (11am-3pm). PART
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“ Statistics should be interpreted with caution as they can be misleading; they can both lie and tell the truth” Statistics are being used everyday to describe things in working and studying areas to show the productivity of the results they are hoping for. Therefore‚ people observe and notice alternative objects the world around. Throughout this fact‚ similarities and differences are such features that could endanger or turned out as advantages. This is called statistics. Explanations
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universally. The scale is chosen depending on the information that the data is intending to represent. The four scales of measurement of data are nominal‚ ordinal‚ interval‚ and ratio. Each plays a different‚ yet very important role in the world of statistic a) Nominal scale Is the lowest level in scales of measurement? Is a way of grouping behavior‚ where actual numbers are simply labels or identifiers. -they do not put subjects in any particular order: no logical basis for the answers in each
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Lecture 1. Descriptive statistics includes statistical procedures that we use to describe the population we are studying. The data could be collected from either a sample or a population‚ but the results help us organize and describe data. Descriptive statistics can only be used to describe the group that is being studying. That is‚ the results cannot be generalized to any larger group. Inferential statistics is concerned with making predictions or inferences about a population from observations
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s |COLLEGE OF THE BAHAMAS | |BUSINESS STATISTICS | |FIRST INTERM EXAM | |COURSE: STAT201
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QNT/351 1) The main purpose of descriptive statistics is to A. summarize data in a useful and informative manner B. make inferences about a population C. determine if the data adequately represents the population D. gather or collect data 2) The general process of gathering‚ organizing‚ summarizing‚ analyzing‚ and interpreting data is called A. statistics B. descriptive statistics C. inferential statistics D. levels of measurement 3) The performance of personal and business investments
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Tab 1----All graphs‚ including the histogram should have an appropriate title and the x and y axis should be labeled. Bin and frequency does not give any information as to what is being represented by the numerical data in the histogram (hint: Electricity cost (in $) and one-bedroom apartments). As Professor Ellis stated in the lectures‚ graphs should be able to stand alone. “A Graph should sing its song!” Bin ranges are correct. However‚ the largest percentage does not lie between 139‚ 179. Both
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STAT 600 Statistics and Quantitative Analysis PROJECT: Stock return estimation The project must be done by 6-15 a.m. October‚ 16th. You should submit your projects before the class begins. This is a group project. Read the course outline for general guidelines. Good luck! The project is closely related to Lectures 1-5 of the class. Today is September 15‚ 2013 and you have just started your new job with a financial planning firm. In addition to studying for all your license exams‚ you have
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Organization of Terms Experimental Design Descriptive Inferential Population Parameter Sample Random Bias Statistic Types of Variables Graphs Measurement scales Nominal Ordinal Interval Ratio Qualitative Quantitative Independent Dependent Bar Graph Histogram Box plot Scatterplot Measures of Center Spread Shape Mean Median Mode Range Variance Standard deviation Skewness Kurtosis Tests of Association Inference Correlation Regression Slope y-intercept
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