A random process X(t) is applied to a system with impulse response: h(t) = te-btu(t) %3D where b > 0 is a constant. The cross-correlation of X(t) with the output 5. Y(t) is known to have an expression: Rxy (T) = te-bru(t) %3D Determine the autocorrelation of output Y(t).
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- 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".)The output of a solar panel (photovoltaic) system depends on its size. A manufacturer states that the average daily production of its 1.5 kW system is 6.6 kilowatt hours (kWh) for Perth conditions. A consumer group monitored this 1.5 kW system in 20 different Perth homes and measured the average daily production by the systems in these homes over a one month period during October. The data is provided here. kWh 6.2, 5.8, 5.9, 6.1, 6.4, 6.3, 6.9, 5.5, 7.4, 6.7, 6.3, 6.2, 7.1, 6.8, 5.9, 5.4, 7.2, 6.7, 5.8, 6.9 1. Analyse the consumer group’s data to test if the manufacturer’s claim of an average of 6.6 kWh per day is reasonable. State appropriate hypotheses, assumptions and decision rule at α = 0.10. What conclusions would you report to the consumer group? (Hint: You will need to find Descriptive Statistics first.) 2. If 48 homes in the central Australian city of Alice Springs had this system installed and similar data was collected, in order to assess whether average daily production in…The Different Effects of Excitation and Inhibition in Neural Circuit (series #1)| Neural circuit consists of polysynaptic neural network, combination of excitatory and inhibitory neurons. This exercise helps to understand the effects of excitation and inhibition in the nervous system. Our body is dynamically active under physiology condition. As a result, there are basal levels of activity such as 70 beats/min of heart rate at resting condition. Either increased or decreased activities are relative to the baseline. The effects of excitation are easily understood---one neuron activates another neuron. However, when inhibition is introduced into circuits, analysis become more complicated. In fact, the numbers of inhibitory neurons in the circuit determine the final effect of the circuit, which can be excitation or inhibition, on the target organ or area. We assume all excitatory and inhibitory neurons are glutamate and GABA neurons, respectively, in this practice sheet. The glutamate and…
- The least-squares regression equation is y- 687.9x + 15,214 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation confficient of 0.7472. Complete parts (a) through (d). 20000 15 ab 35 Bachelors (a) Predict the median income of a region in which 20% of adults 26 years and older have at least a bachelor's degre. $ (Round to the nearest dollar as needed.) (b) In a particular region, 28.7 percent of adults 25 years and older have at least a bachelor's degree. The median income in this region is $38,400. Is this income higher than what you would expect? Why? This is than expected because the expected income i s (Round to the nearest dollar as needed.) (e) Interpret the slope. Select the correct choice below and fill in the answer box to complete your choice. (Type an integer or decimal. Do not round.) O A. For 0% of adults having…A catalog company builds a logistic regression (LR) model to predict the probability that a customer will buy from the catalog during a particular campaign (mailing). The LR model contains 2 independent variables: X1 spend per year in 1000's of dollars (so $2000 will be coded as X1 2) and X2= does customer possess a loyalty card (X2 1 means customer has loyalty card, X2 = 0 means customer does not have the card). Once the model is fitted, the LR coefficients are provided below: = Constant term (BETAO): - 3.5 (negative 3.5) X1 coeff. (BETA1): 0.6 X2 coeff.(BETA2): 1.5 What is the probability that a customer who spends $6000/year and who does NOT have a loyalty card will respond to the campaign? = O About 53% O About 15% About 33% O About 45%The least-squares regression equation is y = 758.4x + 12.9 12,935 where y is the median income and is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7500. (a) Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
- A The least-squares regression equation is y = 720.8x + 14,490 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7743. Complete parts (a) through (d). (a) Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. (Round to the nearest dollar as needed.) G Median Income 55000- 20000+ 15 20 25 30 35 40 45 50 55 60 Bachelor's % QSuppose 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.Plzz give the answer of all questions
- Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe in percentage form), and return on the firm’s stock (ros, in percentage form): log (salary) = β0+ β1 log(sales) + β2roe + β3ros – u. In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary. State the alternative that better stock market performance increases a CEO’s salary. Using the data in CEOSAL1, the following equation was obtained by OLS: = 4.32 + .280 log(sales) + .0174 roe + .00024 ros (.32) (.035) (.0041) (.00054) n = 209, R2 = .283 By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Carry out the test at the 10% significance…Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe in percentage form), and return on the firm’s stock (ros, in percentage form): log (salary) = β0+ β1 log(sales) + β2roe + β3ros – u. In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary. State the alternative that better stock market performance increases a CEO’s salary. Using the data in CEOSAL1, the following equation was obtained by OLS: log(salary) ˆ = 4.32 + .280 log(sales) + .0174 roe + .00024 ros (.32) (.035) (.0041) (.00054) n = 209, R2 = .283 By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Carry out…8. Let consider the data {(−1,0), (2,1), (3,1), (5,2)}. (a) Observe it scatter plot. (b) Approximate the correlation between x and y coordinates. (c) Find the linear regression model by the least square. (d) Find a polynomial that passes through the points.