Consider the time series data set of 200 observationsmodelled as an AR(2) process x₁ = 0.9 x₁-₁-0.2 X₁-2 +Z₁,1-1where z, ~WN 0,0².a) Write the process x, in backshift operator.b) Use Yule-Walker equations or otherwise to find theautocorrelation functions (ACFs) for x, .c) Calculate:(i) Φη .(ii) Þ22.(iii) Standard error for $33.

Given the time series data set of 200 observations modelled as an AR(2) process :…

Answered: Consider the time series data set of…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 33EQ

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Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
Consider a model with an interaction term between being female and being married. The dependent variable is the log of the hourly wage: log(wage) = 0.151 - 0.038 female + 0.1 married - 0.301 female* married + 0.079 educ + 0.027 exper+0.029 tenure (0.072) (0.056) (0.055) (0.007) (0.005) (0.007) n = 536, R2 = 0.461 Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the blanks below which aim at discussing the statistical significance of variables. The test statistic of the interaction term is The critical value at 1% significance level is Then the interaction term statistically significant at 1% significance level. (Hint: to fill the blank make a choice between "is" and "is not".)
Suppose a logistic regression model is fitted for the probability of car ownership for residents of a certain city in Oman (Y=1 if a resident owns a car, Y=0 if a resident does not own a car). Suppose the explanatory variables used are x1=no. of years a resident spent in schooling and x2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident) a) Interpret el and e82 b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.
Suppose that we want to build a model that predicts the group membership of a hurricane, either tropical (0) or non-tropical (1) based on the latitude of formation of the huricane. The response variable is the binary variable Type.new (type of hurricane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm () function. For the logistic regression model, we specify family = "binomial". The data is available at https://userpage.fu-berlin.de/soga/200/2010 data sets hurricanes.xlsx. The R code is #set up filename my.filename 1z|) <2e-16 *** 0.96148 -9.446 0.37283 0.03947 9.447 <2e-16 *** --- *# Signif. codes: 0 '*** 0.001 **' 0.01 '*' 0.05'.' 0.1 '' 1 # (Dispersion parameter for binomial family taken to be 1) Null deviance: 463.11 on 336 degrees of freedom *# Residual deviance: 232.03 on 335 degrees of freedom *# AIC: 236.03 *# Number of Fisher Scoring iterations: 6 What is the interpretation of the p-value of the predictor variable…
2. You have obtained measurements of height in inches of 29 female and 81 male students (Studenth) at your university. A regression of the height on a constant and a binary variable (BFemme), which takes a value of one for females and is zero otherwise, yields the following result: Studentl= 71.0– 4.84×BFemme , R2 = 0.40, SER = 2.0 (0.3) (0.57) a. What is the interpretation of the intercept? What is the interpretation of the slope? How tall are females, on average? b. Test the hypothesis that females, on average, are shorter than males, at the 1% level. c. Is it likely that the error term is homoskedastic here?
A model is built to explain the evolution of spending on tourism and recreation in a certain group of families (Y in USD/person/year). As potential explanatory variables are considered: X1 average annual income per person in the family (in USD), X2 – number of people in the family, X3 – nature of employment of the head of household If X3=1, when the head of the family is self-employed, X3=0, when the head of the family is an employee Based on the collected data, linear correlation coefficients between variables were calculated and obtained 0,84 R, = -0,47 [1 -0,55 0, 62 R= 1 -0,42 0,75 1 Using Hellwig's method, select the optimal combination of explanatory variables for the tourism and recreation expenditure model.
Consider the following estimated model, where the dependent variable is the log of the hourly wage: log(wage) = 0.417 - 0.238female + 0.17educ + 0.0232 exper - 0.00058exper? +0.0295tenure – 0.00059tenure? female 1 if the person is female, and 0 if the person is male educ = level of education, in years exper = level of expertise, in years tenure = duration of tenure, in years Given that the above regression model has 313 observations, then its degrees of freedom is equal to For the same levels of education, expertise, and tenure, women earn exactly % less compared to men, holding other factors fixed.
3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3- Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 a) Find out the equation describing the degradation of lycopene in tomatoes at 15°C, using the "linear regression analysis. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / n ) ΣyΣx%- Σx Σxy b = Ex² - [ (Ex)² / n) ] η (Σχ'- [ (Σx)>/ n) ] )
3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3– Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 Find out the lycopene concentration in tomatoes after 4 months of storage at 15°C. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / ) ΣyΣx?- Σx Σxy Ex² - [ (Ex)² / n) ] n (E x² - [ (Ex)² / n) ] )
Environmental conditions can affect the growth of coral. To study this, a researcher examined a species of coral that is found in the Caribbean Sea and the Gulf of Mexico. At 12 localities, he determined the average annual calcification rate of coral over a period of several years and the average annual maximum sea surface temperature during the same period. Calcification rate affects the growth of coral, with higher rates corresponding to greater growth. The table contains data for these 12 localities. Maximum sea surface temperature (°C) and calcification rate (g cm² yr¯¹) Maximum Sea Surface Temperature 29.4 29.4 29.4 29.6 29.1 28.7 Calcification Rate 1.48 1.53 1.52 1.48 1.31 1.25 Maximum Sea Surface Temperature 29.7 29.5 29.4 29.0 29.0 29.0 Calcification Rate 1.63 1.53 1.46 1.24 1.29 1.12 To access the complete data set, click the link for your preferred software format: Excel Minitab JMP SPSS TI R Mac-TXT PC-TXT CSV CrunchIt! The residuals for average annual maximum sea surface…
Suppose that n = 50, i.i.d observations for (Y₁, X₁) yield the following regressions results: Ỹ= 49.2 + 73.9 X, SER = 13.4, R²=0.78 (23.5) (16.4) Another researcher is interested in the same regression, but makes an error when entering the data into a regression program: The research enters each observation twice, ending up with 100 observations (with observation 1 entered twice, observation 2 entered twice and so forth). Using these 100 observations, what results will be produced by the regression program? Complete the spaces in the equation below. Report the intercept and slope to one decimal place, but report the R2 to two decimal places. Ŷ = + * X, R² =
A scientist is studying three different cultures of bacteria {labeled "A", "B" and "C"} and wants to mathematically model the bacterial change of each type. To do this, the scientist decides to collect data for 25 consecutive days and builds the following scatter diagrams where the horizontal axis corresponds to "t" and the vertical axis to the amount of bacteria present in the culture P = P (t) Help the scientist decide which mathematical model (initial value problem) to use for each of the three types of crops. To do this, associate each of the graphs above with one of the following initial value problems. Crop type A a) dP/dt = k with P(0)=P0 and k>0.b) dP/dt = k with P(0)=P0 and k<0.c) dP/dt = kt with P(0)=P0 and k>0.d) dP/dt = kt with P(0)=P0 and k<0.e) dP/dt = kP with P(0)=P0 and k>0.f) dP/dt = kP with P(0)=P0 and k<0.g) dP/dt = kP(1-P/M) with P(0)=P0 and M, k>0.h) dP/dt = kP(1-P/M) with P(0)=P0 and M, k<0. Crop type B a) dP/dt = k with P(0)=P0 and…

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