# SE-5004L and Econometrics, Semester Two, 2013/4 - Statistics Project Example

Summary
The data is a time series data spanning from 2001 to 2012. The dependent variable represents the per capita on cheese while the independent variable is the unemployment rate from UK.
Per capita data for food has…

## Extract of sample "SE-5004L and Econometrics, Semester Two, 2013/4"

Download file to see previous pages we observe that the coefficient for the unemployment rate is 6.2636, this implies that for any unit change (increase) in employment rate, the per capita on cheese increases per a factor of 6.2636. on the other hand, the constant term is 29.6256, implying that at zero unemployment rate we expect the per capita on cheese to be \$29.6256
From the regression table above, the value of R-Squared (R2) is 0.8894 implying that 88.94% variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model. The remaining 11.06% could probably be explained by the residual (omitted variables) in the model.
The estimated value of b0 (constant term) is positive. The sign of the constant term does not matter (has impact/effect) on the reliability of b1. The value or sign of the constant term does not in any way affect the reliability of b1.
This implies that at zero unemployment rate, the per capita on cheese is assumed to be \$29.6255. the coefficient on unemployment rate is 6.2636, this shows that as the unemployment rate increases so does the per capita on cheese increase by a factor of 6.2636.
Just like in (i) above, we performed the Wald test for the null hypothesis that this coefficient b1=0, the p-value=0.5066>0.05 (significance level), we thus reject the null hypothesis and conclude that
In equation1 we observe that R-Squared is 0.8894 while in equation 2 we find that the value of R-Squared is 0.895. In the first equation it implies that 88.94% of variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model on contrary, in the second equation, 89.5% of variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model. This represents a slight improvement in the model, thus the second equation is more improved (much appropriate and better) than equation 1.
Cite this document
• APA
• MLA
• CHICAGO
(“SE-5004L Statistics and Econometrics, Semester Two, 2013/4 Project”, n.d.)
(SE-5004L Statistics and Econometrics, Semester Two, 2013/4 Project)
https://studentshare.org/statistics/1638403-se-5004l-statistics-and-econometrics-semester-two-20134.
“SE-5004L Statistics and Econometrics, Semester Two, 2013/4 Project”, n.d. https://studentshare.org/statistics/1638403-se-5004l-statistics-and-econometrics-semester-two-20134.
Click to create a comment or rate a document

## CHECK THESE SAMPLES OF SE-5004L Statistics and Econometrics, Semester Two, 2013/4

### Econometrics

...?Econometrics Regression Model Question A The other hypothesis to be investigated by this regression was weather the level of education of women had an impact on their level of availability or participation in the work place. This was to be monitored using the coefficient educ. The availability and increase of other sources of income for the family was also considered to find out weather the same affected their level of commitment at work. This was done using the variable nwifeinc. The estimated coefficients had the signs as expected and were of statistical significance, this was demonstrated clearly buy the patterns displayed by the respective graphs of the variable coefficients. It therefore...
3 Pages(750 words)Assignment

### CRM Project Paper Rubric Spring Semester 2013

...CRM Project Paper Rubric Spring Semester Executive summary The marketing field is driven by an effective relationship. Marketing is about the customer and to achieve effective marketing customer relationship management is the key to success. Good customer relationship management is achieved through the use of marketing principles, formulation of other mechanisms to handle this aspect and more so by applying technology. The most intriguing aspect in the field is how the use of information technology and information systems can immensely contribute to marketing. There are several ways of using these technologies and information systems. The common applications include the customer relationship management systems,...
20 Pages(5000 words)Essay

### MODULE TITLE: - International Business (IB) PROGRAMME: BA SEMESTER: Semester Four ACADEMIC YEAR PERIOD: Feb.2013-May 2013

...?Starbuck’s Foreign Direct Investment Expansion through Licensing Format Starbucks was founded in 1971 as a small coffee store in Seattle’s Pike Place market. By 1992, when it went public, it had become a national phenomenon and was expanding at a break-neck pace (Pride, Hughes & Kapoor, 2009). Patterson et al. (2010) write that the two main reason Starbucks was able to become an international phenomenon are: Firstly, unlike any other coffee brand Starbucks was able to Americanize the European coffee-drinking tradition. Secondly, Starbucks did not just sell coffee, they sold an experience (2010, p. 41) Starbucks has laid significant emphasis on consistency of product, atmosphere and services across their stores globally....
6 Pages(1500 words)Essay

### SE

.... According Krasner (34), exposing children with learning impairments to a second language simultaneously with the primary language lowers the quantity of vocabulary that the children can learn within a certain period of time. For instance, if a ‘normal’ toddler’s cognitive ability can withstand learning of an estimated 20 new vocabularies in a month, when he or she is introduced to two languages at the same time their learning capacity might reduce by half. A child with learning disability, depending on the gravity of the condition would learn fewer words than what a ‘normal’ child can do (Woodcock and Vialle 27). Apart from fewer input and understanding of new words, children learning second language may experience...
8 Pages(2000 words)Research Paper

### Econometrics

...in the dependent variable are explained by changes in the independent variables. Including an insignificant variable as one of the independent variables may minimize the effects of the actual variable causing the variation in the independent variable. Like in the example above, if truly age is not a determinant of income as stated in equation 1, then including it like in equations 2 and 3 will minimize the actual effects of education on income. BIBLIOGRAPHY Greene W. H. (2003). Econometric Analysis. Fifth Edition. Prentice Hall. Pearson Education International. Anderson D. R., Sweeney D. J., Williams T. A. (2005). Statistics for Business and Economics. Ninth Edition. Thomson...
3 Pages(750 words)Essay

### Statistics Assignment 4

...characteristics specific to each stratum. 3. Sampling error occurs when the researcher tries to conclude about the population characteristics after studying the significance of the outcomes on carrying out the tests on the samples from the population. 4. A reliability check is that which finds out if the results so obtained from conducting a study on a sample drawn from a population remains the same when a within-sample test is conducted. On the other hand, validity tests are meant to find out if the topic or subject of research has really been adhered to while gathering information to carry on with the research work. 5. a) Out of two dice rolled on a crap table, the probability that at least one of them...
3 Pages(750 words)Speech or Presentation

### Econometrics

.... This is demonstrated in table 23 below. Just like the above case the lag length was selected using the Ng and Perron method. The most appropriate model that should be employed is the one where no intercept and trend are included since the models do not exhibit unit root. Question one (d) From the above results it is evident that the unit root does not exist when intercept and trend are not included in the model whether the first difference is obtained or not. This means that the original data is statistically significant and hence should be employed in economic analysis. Question two (a) The stationary transformation of the log consumption and log GDP are as demonstrated in figure 3 and...
16 Pages(4000 words)Essay

### Introduction to Statistics and Econometrics

...CORRELATION DOES NOT IMPLY CAUSATION To every scientist, “correlation does not imply causation.” In fact, it is possible that two variables can show the same tendency of quantitative variability despite the fact that there exists no logical and natural relationship between them. On the other side, two variables may trend together as a result of same confounding conditions that cause change in both of them. However, the misguided assumption of causality is the greatest source of error that can occur during the interpretation of results of correlation analysis. The following article can be used to describe a situation where author incorrectly infers causation from correlation. Teen dating may spread teen...
1 Pages(250 words)Essay

### Econometrics

...work Finance and Accounting Econometrics Question Model2 (Model 2) read = 419.7 – 18.6 * noroom – 50.0 * noeng + 29.4 * fiction N = 11984, R2 = 0.204 (1.7) (2.6) (3.1) (0.6) Interpretation of the coefficient for noroom Because noroom is one of the continuous variables, its coefficient is represented by -18.6 which is a constant. If the value of noroom increases, the model value reduces Hypothesis test In order to test for the hypothesis, we use t test as follows t = (1.7-2.6)/(3.1/SQRT(18.6)) = The t score is less than 0.5 at the 5% significance level (Dougherty, 2008). This confirms the null hypothesis, that the parameter is -20 against the alternative hypothesis that the parameter is greater than -20. Question 2 The...
3 Pages(750 words)Coursework

### Econometrics

...Included observations: 97 Variable Coefficient Std. Error t-Statistic Prob. C 0.063194 0.040434 1.562912 0.1214 TB3MS(-1) 1.346506 0.090650 14.85396 0.0000 TB6MS(-2) -0.357690 0.092599 -3.862769 0.0002 R-squared 0.985022 Mean dependent var 2.193814 Adjusted R-squared 0.984703 S.D. dependent var 1.628331 S.E. of regression 0.201392 Akaike info criterion -0.336687 Sum squared resid 3.812524 Schwarz criterion -0.257057 Log likelihood 19.32931 Hannan-Quinn criter. -0.304488 F-statistic 3090.922 Durbin-Watson stat 1.666622 Prob(F-statistic) 0.000000 Do the coefficients make much sense? From the above illustrations, the coefficients are sensible....
1 Pages(250 words)Assignment
We use cookies to create the best experience for you. Keep on browsing if you are OK with that, or find out how to manage cookies.

## Let us find you another Statistics Project on topic SE-5004L Statistics and Econometrics, Semester Two, 2013/4 for FREE!

This Website is owned and operated by Studentshare Ltd (HE364715) , having its registered office at Aglantzias , 21, COMPLEX 21B, Floor 2, Flat/Office 1, Aglantzia , Cyprus.
• FAQ
• Blog
• New Essays
• Essays
• Miscellaneous