portion of a population. We often use information concerning a sample to make an inference (conclusion) about the population. Parameter - describes a characteristic of the population‚ eg: the population variance Statistic- describes a characteristic of a sample‚ eg: the sample variance Frequency Distribution and Histograms Class - a collection of data which are mutually exclusive Frequency distribution - a grouping of data into classes Relative frequency distribution - calculates the
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GPA reported. The hours or sleep and the GPA of the students were entered into SPSS ’s spread sheet and are shown on the next page. E720 Notebook Assignment: Correlation Kandell 2 Dependent and Independent Variables In correlational analysis we do not call one variable dependent and the other independent. However‚ Hours of sleep will be placed on the X-axis (abscissa) and GPA will placed on the Y-axis (ordinate) Is there a correlation between Hours of sleep and GPA in graduate students
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University of Southern California Department of Economics ECON 317 Introduction to Statistics for Economists Prof. Safarzadeh Assignment # 2 Student Name: ________________ Answer all the questions on the spaces provided. Underline your answers and show your calculations and work on the tables. Item |Speed |Mileage | | |X - X |(X- X)2 |(Y-Y) |(Y-Y)2 |(X-X)(Y-Y) | | | |1 | 30 | 25 | | | | | | | | | | |2 | 50 | 20 | | | | | | | | | | |3 | 35 | 23 | | | | | | | | | | |4 | 45 | 21 | |
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and b E. neither a nor b 2.Assume that stock market returns do follow a single-index structure. An investment fund analyzes 500 stocks in order to construct a mean-variance efficient portfolio constrained by 500 investments. They will need to calculate ________ estimates of firm-specific variances and ________ estimates for the variance of the macroeconomic factor. A. 500; 1 B. 500; 500 C. 124‚750; 1 D. 124‚750; 500 E. 250‚000; 500 3.Suppose you held a well-diversified portfolio with a very
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Descriptive Statistics Kelly Calle QNT/561 February 15‚ 2015 John Carroll Descriptive Statistics and Interpretation Descriptive statistics is the term given to the analysis of data that helps describe‚ show‚ or summarize data in a meaningful way. Descriptive statistics does not allow conclusions beyond the data analyzed or reach conclusions regarding any hypotheses made. It is only a way to describe the data gathered. Descriptive statistics allows data to be presented in a more meaningful way
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probability (7%) 1. Let the random variable X follow a Binomial distribution with parameters n and p. We write X ~ B(n‚p). * Write down all basic assumptions of Binomial distribution. * Knowing the p.m.f. of X‚ show that the mean and variance of X are = np‚ and 2 = np(1 – p)‚ respectively. 2. A batch contains 40 bacteria cells and 12 of them are not capable of cellular replication. Suppose you examine 3 bacteria cells selected at random without replacement. What is the probability
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and (C) The range of the data that would contain 68% of the results. (5 points). Raw data: sales/month (Millions of $) 23 45 34 34 56 67 54 34 45 56 23 19 Descriptive Statistics: Sales | Variable | Total Count | Mean | StDev | Variance | Minimum | Maximum | Range | Sales | 12 | 40.83 | 15.39 | 236.88 | 19.00 | 67.00 | 48.00 | Stem-and-Leaf Display: Sales Stem-and-leaf of Sales N = 12 Leaf Unit = 1.0 | 1 | 1 | 9 | 3 | 2 | 33 | 3 | 2 | | 6 |
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a.The mean return should be less than the value computed in the spreadsheet. The fund’s return is 5% lower in a recession‚ but only 3% higher in a boom. The variance of returns should be greater than the value in the spreadsheet‚ reflecting the greater dispersion of outcomes in the three scenarios. b.Calculation of mean return and variance for the stock fund: (A) (B) (C) (D) (E) (F) (G) Scenario Probability Rate of Return Col. B Col. C Deviation from Expected Return Squared Deviation Col. B
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Observation = estimated relationship + residual: yi =+ ei => yi = b1 + b2 x + ei Assumptions underlying model: 1. Linear Model ui = yi - 1- 2xi 2. Error terms have mean = 0 E(ui|x)=0 => E(y|x) = 1 + 2xi 3. Error terms have constant variance (independent of x) Var(ui|x) = 2=Var(yi|x) (homoscedastic errors) 4. Cov(ui‚ uj )= Cov(yi‚ yj )= 0. (no autocorrelation) 5. X is not a constant and is fixed in repeated samples. Additional assumption: 6. ui~N(0‚ 2) => yi~N(1- 2xi‚ 2)
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Part 1 of 1 - | Question 1 of 10 | 1.0 Points | Consider the following scenario in answering questions 1 through 4. Results from previous studies showed 79% of all high school seniors from a certain city plan to attend college after graduation. A random sample of 200 high school seniors from this city reveals that 162 plan to attend college. Does this indicate that the percentage has increased from that of previous studies? Test at the 5% level of significance. State the null and alternative
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