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Task 1
1. With respect to model size calculation, the data used in this task was drawn general population of 50 individuals or entities. The results show that the average market cost is 781 standard deviation 80.09 while the average house age in years is 17 with the normal deviation 11.24. Additionally, the market center is not set in stone to be 792 while the center for the time of the house is 14 years. Also the top quartile and third quartile market the cost and season of the house are given 732.98; 826.41 and 9.52; 22.73 separately.
In addition, the interquartile range for the glebe market stall cost and age of the house is considered to be 93.43 and 13.21 independently, while the basic and most obnoxious potential profits of market costs and duration are given by 625; 971 and 1; 45 independently, (Abbott, 2016). The compasses for the market price and age of the house in years are $346,000 and 44 years however, the coefficient still in the air must be independently 0.10 and 0.66. In addition, the market price and season of the house in years independently recorded a mean value of 172.81 and 21.97.
2. For inspirations determining the characteristic state of flows for two elements, data imaging techniques were used including the use of a histogram and repeat variance drawn. From the drawings, the conditions of the elements help to conclude the types of appointments as indicated under. Market costs are unfavorably tilted. As previously mentioned, negative circulation occurs when the tail is on one side, and in this circulation the mean is usually more modest than the mean, as was shown at contour measure (Gul, 2021). House time in years is strongly inclined. Reliable, usually positive transfer happens when most of the information foci are on one side of the chart. In this mode of transport, the diameter is usually larger than the middle.
From aggregate measurements for market price ($000) to some extent 1 mean = 768.8453, median = 16.4858 and the mode is 8.
Thus, Medium > Mode
768.8453 > 0
So the glebe market stall cost cycle is positively skewed.
From summary measurements for house age (in years) in limited range 1 mean = 17.75086,
Median =16.48585 and mode is 8 so for positive slope
Mean > medium> mode
17.75086>16.48585>8
The spread over length of stay (in years) is therefore strongly skewed.
3. The results obtained from the above table show that the typical market price is not unique from the year 777. We hereby recognize invalid speculation.
H0: μ=777
H1: μ≠777
Significance level
a=0.05
Test statistics, Z = X' –μ/σ √n
Calculation
Z = 768.8453777*71.96023/√400 = −8.145/3,598 =2.266
Basic region, Za/2=∓1.96
It is concluded that Z determined =  2.266 which is below 1.96 so we reject our invalid speculation the population average is 777, so we have no evidence to confirm him in the blessings of choice speculation.
4. From the discoveries, the results show that 64% of the lower pvalues show the probability mean is more than the hypothesized value which is 758.82 while 36% shows pan motivation for upper region that the probability mean value is not exactly the hypothetical value.
X' –Zα/2, Σ = √n. <μ
2σ/√n = 768.8453−1.96 71.96/√400, <μ<768.8453+1.96 71.96/√400
761.79<μ<775.89
We are 95% confident that our population mean lies between 761.79 and 775.89.
What's more, the test two followed pvalue of 0.7226 shows that the average rise of speculative qualities does not decrease between the values of the margin of confidence; 758.82803.22 consequently does not reflect the true average population.
5. The income model will consider how the market price ($000) is affected by various variables such as Sydney Respect Index, Total Square Meters and Age of the House (quite a long time). In this model, the market price ($000) is the dependent variable, while Sydney esteem index, total square meters and house age (and long time) are independent variables.
In our model we have four factors which are market value, Sydney cost record, all out the number of square meters and the age of the house in years. Market costs are dependent on a variable in our model and the Sydney cost list, count square meters and house age (in years) are autonomous factors in our model. The test size for each variable is 400. Our model is
Market price=β0+ β1
(Sydney cost index)+β2
(Total number of square meters)+β3, duration of the house∈ years.
The dependent variable,glebe market stall cost, has a measurement unit of USD, while the variable "number of square meters in full scale" has a rating unit of square meters (Conrad, 2016). In addition, it is the measurement unit for the variable length of stay in years. Finally, unit evaluation for the variable, "Sydney esteem index" was not mentioned, most likely it will be USD in addition.
6. The link between the Sydney value index and the market price ($000) shows that there is a weak negative ( 0.1631) relationship as shown in the diagram below. It follows that as the Sydney cost Index is expanding, its market price ($000) has also slightly improved. In this case, the dependent variable is regularly plotted on the ypivot, while the autonomous variable is rendered in xhub.
Summary of Statistics 
Regression 
Multiple Regression 
0.99 
R square 
0.99 
No. of observations 
50 
R Square (Adjusted) 
0.994 
Error (Standard) 
6.07 
Variation 
Degree of freddom (f) 
Sum of squares 
Sum of squares (Mean) 
Calculated F 
Tabulated F 
Residual 
46 
1699.7 
36.95 
0 
0 
Regression analysis 
3 
312570.6 
104190.2 
2819.6 
0 
Total 
49 
3114270.4 
From the result below, only Total Square Meters is a decent indicator of the market costs, while the Sydney Value Index and housing tenure are terrible indicators of market costs.
The table above shows that the consequences of different models of relapse. The value of the catch is 410.4041 means that, assuming all autonomous factors are zero, the market value of the property remains corrected to $410.4041. The Sydney price file is  that's what 0.00091 is showing assuming we increase by one unit the cost pool is reduced by $0.00091 in the market value of the property. The coefficient of the number of square meters is 1.746, which shows that if we increase one unit of square meters, this will expand market price of the property. In addition, the coefficient of a sufficiently old house is 0.052, which means that we will increment one year it will extend the glebe market stall cost of the property assuming we decrease by one year in the period we have it moreover, it lowers the market price of the value (Gul, 2021). The pvalue is below 0.05 for the number of square meters and catch, so these coefficients are not critical. The numerical R is 0.9968 is around one, which shows an ideal relationship between the market value property and every free factor. R Square shows that 99.3% of the information fits into the relapse model.
8. Equation for Regression analysis
Market cost = 430.685 +1.655* Total square meters
9. The market price of the house on the smallest side is $430,685.
The recurrence in 8 shows that assuming we increase one unit square meter in the model, it will an increase of 1,744 units in the market value of the property. Assuming we reject one unit square meter, then it will reduce the market value of the property by 1,744 units. Capture 411.6369 appears that at an increase/decrease of zero units in square meters, the market value of the house is fixed 411.6369.
The simple linear regression model that aimed to incorporate more than one free factor is known as the multiple relapse model. It is more accurate than straight relapse. Reasons for various relapses are: I) organization and control ii) anticipation or estimation. The main advantage of different recidivism models is that they give us more data available to those of us measuring the dependent variable. It allows us to adjust the curves as well as the lines. In the event that the investigated variable relies on a solitary variable, it can be centered on the basic model of relapse. Still, even if there is a chance that the variable under consideration relies on more than one factor, this may not focus on the underlying model of relapse at any point. This study can be easily visualized using different models of recidivism (Kishore and Trivedi, 2016). As a general rule, any factor to be concentrated usually depends on many factors, but typical practice is to subject a variable to a lone variable. Likewise, different models of recidivism need to be viewed in order to obtain discoveries with greater precision. In the event that the fluctuating test is rejected, the next question is what relapse coefficients are responsible for rejecting invalid speculation. Logical factorsrelating to such relapse coefficients are significant for the model.
10) 0.996604 means that the autonomous variable apparently makes sense of the dependent variable 99.6% of the time.
Stats 
Regresssion Values 
R Square 
0.9944 
Multiple R 
0.9972 
Standard Error 
6.02 
Observations 
50 
R Square (Adjusted) 
0.9943 
11) In the tables below, there is the 95% confidence interval for relapse propensity
Lower CI = 1.730522
Upper CI = 1.757845
Coefficients 
Standard error 
tStat 
pvalue 
95% lower (CI) 
95% Upper (CI) 

Intercept 
4.1163 
1.489 
276.37 
0 
408.70 
414.56 
Square R 
1.744 
0.006 
250.99 
0 
1.730 
1.757 
The upper and lower relapse bounds show that we are 95% confident that our slope of the relapse model lies somewhere between 1.730533 and 1.757845
The slope of the multiple direct relapse model is much lower than the basic direct relapse model because the direct relapse model is more accurate than the multiple direct relapse model.
12) For both models, the good "R Square" tightness is the same, meaning 99.37% for both models 99.37% of the information fits our relapse models.
Market price = 430.685 +1.655 multiplied by the total number of square meters
=430685 + (1.655*250)
= 431098.8
The assumed model is completed to be legitimate.
13) Y = 411.6369 + 1.744X
Y=411.6369+1.744 (300)=934.982
With an extension of 300 square meters, the market cost of the house is 934,982
14) As shown in the table below, the Pvalue is 0 determined from the t test is 0, which is more remarkable than an alpha of 0.05, so we reject our null hypothesis that plot size in square meters is valuable predict the market price of the house in dollars.
Coefficients 
standard deviation error 
tStat 
pvalue 
95% lower (CI) 
95% Upper (CI) 

Intercept 
4.1163 
1.489 
276.37 
0 
408.70 
414.56 
Square R 
1.744 
0.006 
250.99 
0 
1.730 
1.757 
15) (i) H0: β1+β2+β3=0
Variables 
Coefficient 
standard deviation Error 
tStat 
pvalue 
95% Lower (CI) 
95% Upper (CI) 
Price of Sydney 
0.0009 
0.0106 
0.0858 
0.931 
0.021 
0.02 
House’s Age 
0.052 
0.0277 
1.888 
0.0059 
0.0021 
0.106 
Pesteem is below 0.05 for the number of square meters, so β2 is measurably noncritical and the PValue is huge for the set of Sydney values and the house period. In this sense, we reject our invalid theory for the number of square meter