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Statistics 1. Income level is a significant determinant of the home environment of students
Statistics 1
Income level is a significant determinant of the home environment of students, school that students attend, as well as the leisure activities and choice of entertainment. Imperatively, a student’s score in S.A.T tests is partly influenced by the IQ of the student, as well as the degree of exposure to learning opportunities. Students from families with lots of disposable income demonstrate higher scores than students from low income families. Essentially, students from high income families manage high score in tests; as their parents can afford tuition fees for their kids. Further, such kids are frequently exposed to educating and learning opportunities (Miles & Shevlin, 2000).
On the contrary, kids from low income parents live in environments that are not favorable for learning. Most low income students miss lessons and learning opportunities due to absenteeism. Therefore, the positive correlation between performance and level of income of the parents is absolutely true (Correlation and Regression Analysis, 2012). It is imperative to note that, test scores in students is influenced by multiple factors; thus correlation is not sufficient to describe the cause causes the result to performance.#2Correlation is a measure of the extent or degree to which a couple of variables is related. On the same note, correlation describes the linear association between two variables as x and y. On the contrary, regression describes the exact association (linear), which exist between a couple of variables as x and y (Miles & Shevlin, 2000). Both regression and correlation are similar in that the two are concerned with the examination of the association between a couple of variables, as well as whether an alteration in one variable results, in alteration in the second variable. Correlation is preferred in qualitative intellectual inquiries, especially in social sciences while regression is mostly utilized in quantitative studies; as correlation is suited in the handling descriptive data (Correlation and Regression Analysis, 2012). Criterion variables facilitate the description of the dependent variables in different statistical contexts. On the same note, predictor variables are vital in a regression equation; as they facilitate the anticipation and prediction of the value of other variables.
References
Correlation and Regression Analysis. (2012). S.l.: Sage Pubns Ltd.
Miles, J., & Shevlin, M. (2000). Applying regression & correlation: A guide for students and researchers. London: SAGE.
Statistical Tools in Marketing
Statistical Tools in Marketing ResearchANOVA
This is a statistical tool of analysis that is used to separate total variability existing in a set of data into two components: systematic and random factors. The random factors have no statistical influence on the data set under study, while on the other hand systematic factors do. The ANOVA test is used to establish the effect of independent variables on the dependent variables in a regression analysis. It is a guide in telling whether or not an occurrence was most probable due to a random chance of variation. ANOVA test is a statistical technique used to determine the existence of variations among various population means. It is not used to determine how variances are different but to determine how means of a data set are different. It is the initial step in identifying the factors that influence a given data set. After performing the ANOVA test, it becomes possible for any data analyst to perform other analysis on systematic factors that statistically contribute the variability of the data set. The results of ANOVA test analysis can further be used in F-test analysis to test the significance of the overall regression formula.
ANOVA test can be divided into three parts depending on the kind of data under analysis: ANOVA: Single factor, ANOVA: Two factor with a replication, ANOVA: Two factor without a replication. ANOVA: Single factor performs an analysis on a data having two or more samples. The analysis provides an hypothesis test on each data from where each sample is drawn. In a data set where there are only two samples, a worksheet function could equally be used, while in a case of more than two samples, the use of a worksheet may not be convenient. The next part is ANOVA: Two-factor with a replication, which majorly used when the data under analysis is classified into two different dimensions. For example, in an experiment aimed at measuring the heights of plants, the plants may be treated with different brands of fertilizer and may also be put under different temperatures. The ANOVA tool can then be used to test whether the plants’ heights and the different brands of fertilizer are drawn from a similar underlying population. The next type is ANOVA: Two-factor without a replication, which is used in a situation where data are classified under two different dimensions; the same case with the two-factor case with replication. However, for this tool there is an assumption that there is only one observation for every pair.
ANOVA is a particular type of statistical hypothesis testing that is heavily used in analyzing an experimental data. Statistical hypothesis test is used to make decisions using data. A test result is called statistically significant incase it is deemed not likely to have happened by chance. A statistically significant result when the p-value is less than a significance level justifies rejection of a null hypothesis, but only in a case where the null hypothesis prior probability is not low. In the application of ANOVA test, the null hypothesis is described as all groups being random samples of a similar population, implying that all the treatments have a similar effect. Rejecting the null hypothesis indicates that different treatments lead to altered effects. ANOVA involves the synthesis and analysis of various ideas and it is for that matter used for multiple purposes. ANOVA, as an exploratory data analysis, is a system of additive data decomposition with its sum of squares indicating the variance for every component of the decomposition. It can also be used in comparing mean squares and the F-tests and thus allowing testing for a clustered sequence of models in the marketing system. It is relatively robust and computationally elegant against violations of existing assumptions giving it the industrial, strength to carry out statistical analysis in the market environment. Due to its ability to analyze numerous and complex sets of data, ANOVA has for a long time enjoyed the prestige of being the most used statistical tool of analysis in psychological research. It is also the most useful tool in statistical inference data in the marketing environment. Analysis of variance can be studied by several approaches and the most common approach is the use of linear model, which relates the responses to the blocks and treatments. However, the model is normally linear but it can be non-linear across other factor levels. Interpretation of the data is normally easy in cases of a balanced data across other factors but deeper understanding is required in a case for unbalanced data.
The T-test
This is a statistical test used in the comparison of means of two treatments or samples, even if they possess varying numbers of replicates. In simple terms, it compares the actual difference existing between two means of a particular data set. It can be used to tell if two data sets are statistically different from one another, and is often applied in situations where the test statistic follows a normal distribution and if the scaling value term value in the test statistic is known. The t-test considers the t-distribution, degrees of freedom, and t-statistic to determine the probability (p-value) that can be used to tell whether the means of the population differ. The t-test statistic is very popular as millions of t-test analyses are performed on a daily basis in the marketing research industry. The t-test was formulated to test the initially developed hypothesis. For example, it can be put into use to determine whether two batches of wine are equally good. There is a large variety of t-test and the most commonly used varieties today are:
One sample t-test: this is used to test whether the population mean has a pre-determined value or not. For example, a company can specify that all new concepts must achieve a score of 50 before proceeding to the next stage of testing. One sample t-test can be used in this case to tell if any of the new concepts can significantly test below this standard.
Two-sample test: this is the most commonly used and also misused type of t-test. It is used to test for differences in the means of two populations. For example, this test can be used to determine whether or not there are significant differences in the way women and men score the new concept.
Paired t-test: this test can be used in a situation where two measurements come from the same source to determine whether or not there is a difference between the means of the two measures. For example, if it is known how much a particular respondent liked a specific concept (concept A) and how much he liked another concept (concept B), the paired t-test can be used to tell whether or not there exist a significant difference in the preferences.
In a t-test statistic that is used to compare the means of two independent experiments, there is need to observe the following assumptions:
Each of the populations under comparison should have a normal distribution. This can be established by putting the populations through a normally test or graphically assessed using normal quintile plot.
When using the original student t-test, the two populations under comparison should have a similar variance. This can be tested using Levene’s test, brown-Forsythe test, F-test; or tested graphically using a Q-Q plot. Incase the sample sizes of the two groups under comparison are equal, the original student t-test is highly recommended in analysis of unequal variances. There is another test called Welch’s t-test, which not sensitive to equality of variances.
The data that is used to do the testing should be independently be sampled from the populations that are being compared. This is generally not possible to test from the data, but incase the data are independently sampled; the classical t-tests may show misleading results.
Two sample t-tests involve paired samples, independent samples, and overlapping samples. The t-test can also be divided into unpaired two-sample t-test and paired t-tests. Paired tests are a type of blocking and are more powerful than unpaired tests. In a different context, the paired t-test can also be used in reducing the impact of confounding factors in observational studies. There is also the independent t-test, which is normally used in situations where two separate sets of identically and independently distributed samples are obtained from every population being compared. Overlapping sample t-test on the other hand is used in situations where there paired samples but have certain part of data missing in them. This test is commonly used in commercial surveys. For example by polling surveys.
Chi-Square test
This is a statistical test that is normally used to carry out a comparison between the observed data and the data that a researcher or a data analyst expects to get in relation to a specific hypothesis. In any form of a data distribution, there are generally two kinds of random variables that in turn yield two kinds of data: categorical and numerical. The chi square statistic is used in investigating whether categorical variable distributions differ from each other. Categorical variable basically yields data in form of categories while numerical variable on the other hand yield data yields data in numerical forms. The use of chi square test can be used in several situations in the market for decision making: 1) Are all designs equally preferred? 2) Are all brands equally preferred? 3) Is there any relationship existing between brand preference and income level, 4) Is there any relationship between the size of the washing machine purchased and the family size? 5) Is there any relationship between the type of job chosen and the educational background? These questions can be answered by the use of chi square analysis. The first two questions can be answered by chi-square test for the goodness of fit while questions 3, 4, and 5 can be solved by the use of chi square test for independence. It is important to note that the variables used in the Chi-square analysis are usually nominally scaled. Nominal data are known by two names; attribute data and categorical data.
Works cited
Schmuller, Joseph. Statistical Analysis with Excel for Dummies. New York: Wiley, 2013. Internet resource.
Rumsey, Deborah J. Statistics for Dummies. Hoboken, N.J: Wiley Pub, 2011. Internet resource.
Nelson, Stephen L. Excel 2007 Data Analysis for Dummies. Hoboken, N.J: John Wiley & Sons, 2013. Internet resource.
Janert, Philipp K. Data Analysis with Open Source Tools. Sebastopol, CA: O’Reilly, 2011. Internet resource.
Ott, R L, Michael T. Longnecker, and Jackie Miller. An Introduction to Statistical Methods and Data Analysis. Boston: Brooks/Cole/Cengage Learning, 2010. Print.
Janert, Philipp K. Gnuplot in Action: Understanding Data with Graphs. Greenwich, Conn: Manning, 2010. Print.
Warden, Pete. Data Source Handbook. Sebastopol, Calif: O’Reilly Media, 2011. Internet resource.
McKinney, Wes. Python for Data Analysis. Sebastopol, Calif: O’Reilly, 2013. Print.
Statistical research Report on Deaths on the Road in Queensland Australia for 2012
Statistical research Report on Deaths on the Road in Queensland Australia for 2012
This report presents statistical research Report on Deaths on the Road in Queensland Australia for 2012.
In the last four years, there has been significant increase in fatalities in the Queensland where the 17-25 age groups has recorded the prime rate which accounts for 13% of the population. There has been increase in road accidents in Queensland Australia which has consumed many lives in the recent past. The frequent road carnages in Australia have had serious social and economic impact in the society.
This report is very essential in providing crucial information to the Commissioner of Police. The information is very critical as far as addressing the frequent road carnage is concerned. The media exposure has also had a very huge influence on The Department of Infrastructure and Transport thus tarnishing the Police Department and all other departments related with transport.
A REPORT FOR ASSESSMENT TASK NUMBER TWO
(BSBRES401A)
PREPARED BY:
Name
Date
Executive SummaryThis report is about causes of road carnage in Queensland which are classified into human behavior, road users and heavy trucks. Human behaviors particularly amongst the drivers aged between 17 and 25 are the major factors that contributed to a high number of accidents and continuous road carnage in Queensland in 2012. It further provides recommendations on how the fatal road accidents can be reduced such as through ensuring drivers’ competence and behavior control as well as guaranteeing road and vehicle safety.Contents
TOC o “1-3” h z u Executive Summary……………………………………………………………………….3
1 Introduction……………………………………………………………………………..5
1.1 Aim……………………………………………………………………………………5
1.2 Authorization ………………………………………………………………………….5
1.3 Sources…………………………………………………………………………………5
2 Findings………………………………………………………………………………….6
2.1 Heavy trucks and buses………………………………………………………………..6
2.2 Human behavior……………………………………………………………………….7
2.3 Road users……………………………………………………………………………..7
3 Analysis………………………………………………………………………………….8
4 Conclusion………………………………………………………………………………9
5 Recommendation………………………………………………………………………10
5.1 Improving and regulating drivers’ behavior and competence………………………..10
5.2 Improving of the safety of roads and vehicles……………………………………….11
6 Bibliography……………………………………………………………………………12
7 Appendices……………………………………………………………………………13
Introduction
Australia’s transportation department has provided a framework for national collaboration that is aimed at reducing the per capita rate of road deaths. The issue of road safety is very important issue that requires a holistic view and solution to the devastating problem. Although Australia was among the first countries that ceremoniously adopted the safe system approach to ensure road safety, there have been various elements that have led to serious loss of life. It is therefore important to address these serious concerns that have resulted into death and serious injuries.
Casualty reduction is very much achievable and therefore the high rate of deaths particularly the young people must be addressed. The various causes of deaths such as careless driving and drunk drivers must be properly analyzed and stopped through careful application of the laws and regulation. Various causes of road carnages as well as solutions to stop these killings are discussed in this report. Some of the causes of deaths on the roads include human behavior, road users, heavy trucks and buses.
Aim
The aim is to investigate various causes of road carnages in the Queensland and the possible solutions to reduce these fatal killings.
1.2 Authorization
This report was authorized by the police department in conjunction with transportation department in order to curb road killings.
1.3 SourcesThis report has been made successful through the great contributions from Queens land Department of Transport and Main Roads.
FindingsIncreased road accident posed a great threat in Queensland especially among the young people aged between 17 and 25 years. Despite many controls and measures put in place, there are no positive progresses as far as controlling or minimizing road carnages is concerned. According to this report, total fatal crashes in 2012 increased compared to 2011. Some of the major fatalities were contributed by driver, passengers, motorcycle riders and heavy freight crashes. Bicycle riders facilities were the least amongst facilities with only two incidences recorded in 2012 compared with two incidences in 2011. Heavy trucks, human behavior and road users largely contributed to several road carnages in Queensland. Queensland was among the states that recorded highest number of road accidents thus leading to the importance of the report (Department of Infrastructure and Transport, 2012).
2.1 Heavy trucks
Within the 12 months ending September 2012, there were almost 15% of fatal crashes that involved heavy trucks and buses where heavy trucks were further categorized as rigid trucks and articulated trucks. Fatal crashes caused by heavy trucks attributed to articulated trucks decreased by o.8% while lethal crashes involving heavy rigid trucks increased by 14.3% compared to the year 2011. The totals of heavy truck fatal crashes were 28 in 2012 compared 25 cases in the year 2011. Fatal crashes that were contributed by articulated trucks were categorized into various speed zones as followed 0-60, 70-90, 100, and >110 km/h which caused the deaths as follows 4, 0, 23 and 2 respectively. Fatal crashes that involved heavy rigid trucks by jurisdiction were categorized into various speed zones as follows 0-60, 70-90, 100 and >110km/h which subsequently caused 9, 5, 7 and 0 deaths respectively (Department of Infrastructure and Transport, 2012).
2.2 Human Behavior
Drinking and careless driving has also been blamed on the ever increasing deaths caused by road accidents. People tend not to be responsible and therefore do not take driving as serious as it should be. Over speeding contributed to almost 45% fatal crashes out of all the human behaviors and closely followed by drunken drivers, fatigue, failure to wear safety belts and use of a mobile phone. These are the major causes of fatal crashes yet they can be avoided. These causes of fatal killings have however increased in 2012 as compared to 2011. Victims of these groups are mainly young people of ages from 17 to 25. Most of them do not even know or understands the driving rules and regulation in Queensland. Speed zone and jurisdictions were classified into 0-60, 70-90, 100 and > 110km/h and caused deaths as followed 96, 54, and 96 and 9 (Department of Infrastructure and Transport, 2012).
2.3 Road users
Road users include the drivers, passengers, pedestrians, motorcyclists and cyclists. Amongst them, drivers are really affected. Most fatal road accidents are caused by drivers which comprises of almost 49% of the total road users. They are closely followed by passengers at 20%. However, road users such as pedestrians are always falling victims due to careless use of roads. The total death toll for the road users amounted to 280 with the number of drivers’ death recorded amounting to 125. Motorcyclists and passenger’s death recorded were 60 and 58 respectively while cyclists were 10. There were also variances when it came to age group where the youths recorded highest number of deaths. The age groups were categorized into 0-16, 17-25, 26-39, 40-59, 60-69 and >70 years where the number of deaths recorded for every group were 15, 72, 70, 64, 29, and 30 respectively (Department of Infrastructure and Transport, 2012). Refer to appendix A.
3 AnalysisIt is clear that most of the road carnages are caused by human behaviors in Queensland. Use of phones and over speeding are among the greatest causes of road accidents according to the report. Over speeding has largely contributed to the human behavior and its effect is so great. Many lives have been lost through this kind of human behavior. It contributed to almost 45% of human behavior and was rampant amongst the age group between 17 and 25 years. Human behavior caused fatal accidents between the speed zones of 0-60km/hr. and 100km/hr. (Department of Infrastructure and Transport, 2012).
Road users who were largely affected by the road accidents in Queensland were drivers perhaps because they are always at work driving. The fatal accidents contributed by drivers contributed about 49% of the road users. The numbers of deaths of drivers who have died from road carnages were 125. The age groups affected much were between 17 and 24 with death toll of almost 72. This young age group drives carelessly without paying much attention to rules and regulations of the road use (Department of Infrastructure and Transport, 2012).
Heavy trucks have also caused several accidents in the Queensland especially due to carelessness and failure to adhere to rules and regulations of the road use. Many drivers do not consider things such as the distance between the truck and the vehicle ahead of them. Some of the reasons behind the heavy trucks and buses’ accidents are due to inadequate distance between them as well as the adverse weather conditions. Drivers also do not consider the gross mass of their vehicles especially the speed in which they cross the bridges. A good percentage of drivers of trucks and buses also do not use low gears while driving down the steep routes. The most dangerous speed zone was 100km/hr. which has led to highest number of deaths in both articulated and heavy rigid trucks (Department of Infrastructure and Transport, 2012).
4 Conclusions
This report has successfully achieved its aim by pointing out at some of the causes of road accidents in Queensland. It is important to note that most of the age group affected by road accidents was 17-25 years. They were subject in almost every cause such as the human behavior, road users and the heavy trucks. The young age group is fond of breaking rules and regulations set by the department of transportation. It is in this age group that people make phone calls and listen to loud music while driving thus detracting their attention and eventually causing accidents. The age group of 17-25 years is involved much in over speeding cases too.
In general, people do not obey the laws and over speeding is one of the major causes of road carnages. Other human behaviors such as drunkard drivers and careless driving have had a great damage to many cars. Drivers and motorcyclists are the major players and agents of road carnages in Queensland simply because of their negligence. However, there are other causes of road accidents such as conditions of the vehicle and adverse weather conditions. Road accidents have very serious effect on the economy of Queensland particularly in loss of life, permanent impairment and property damage due to inability to recover the productive brains and resources that are lost.
5 Recommendations
A serious action need to be taken to reduce the high number of innocent peoples’ deaths and properties on the road through road accidents. This report recommends Improving of drivers’’ behaviors, improving the safety of roads and improving vehicle conditions.
5.1 Improving and regulating drivers’ behavior and competency.
The human behavior particularly the drivers’ behaviors should be highly controlled and addressed seriously. The report recommends the following:
There should be much greater enforcement of the existing laws by the police by using the computer system that can detect the speed of the vehicles as well as introduction of heavy penalties on offenders.
Anti-drunk –driving should be enforced strongly especially during the weekends and any other holidays as well as introduction of breathe testing gadgets to deter drivers from drinking.
Hot spots and every minor road within Queensland should have speed checks through introduction of more speed cameras. All the drivers should undergo proper driving tests and limit the number of provisional licenses an individual may have.
There should be proper institutions that provide continuous training of drivers particularly drivers who have committed offense at their own cost and a yearly campaign that discourages drivers from over speeding and drunk driving.
All driving instructors should have National driver’s Instructor’s qualification and introduction of safe driving education in schools as well as funding the Driver Training Simulator as a way of boosting driver competence and behavior.
5.2 Improving of the safety of roads and vehicles
Ensuring proper road conditions should be given first priority such as all planned road projects and safe overtaking opportunities and reduction of tight turns.
All junctions and road signs should be easily identified by using standard signs and using very clear road markings.
There should be sufficient footpaths and lowering of speed limits for minor roads as well as carrying out detailed analysis on the ways of reducing road carnages.
Proper vehicle safety information published by the manufacturers should be publicly displayed as well mandatory inclusion of traction control.
All vehicles must have speed governors and heavy vehicles ‘weights must be randomly checked.
All the vehicles should be properly analyzed for safety defects and the use of “bull bars” should be banned as use of daytime running lights made compulsory.
6 Bibliographies
Department of Infrastructure and Transport, Australia. (2012).7 AppendicesAppendix A
Deaths by jurisdiction and road users
NSW Vic Qld SA WA Tas NT ACT
2008 374 303 328 99 205 39 75 14
2009 453 290 331 119 190 63 30 12
2010 405 288 249 118 193 31 49 19
2011 364 287 269 103 180 24 44 6
2012 376 282 280 94 185 33 48 12