Correlation Between TIME And SIZE

The purpose and benefits of organising data so that it can be analysed as you will be turning this data into information. Data is commonly known as information which is lacking meaning, therefore it cannot be analysed easily, however information is much more meaningful. We can filter the data and use it as needed which saves time scrolling through lots of unnecessary data. The benefits of organising data are that is can be easier to read and to understand. It also makes it quicker to find the information you need from it.

Feedback: A strong correlation exists when the plotted ordered pair points are close to the line of best fit. If the points line up exactly on the line of best fit, there is a perfect correlation. The

After entering the data, it must be reviewed to catch any errors. Cleaning the data is done with basic steps. Of course the data is imported from the source, but there should always be some backup involved in order to keep the integrity of the data. In some cases the next step would be manipulation or spellchecking. This depends on what kind of data is involved. However, in this case spell checking is not necessary, but manipulation is. For instance with manipulation, they may have to add a column or two, or even add a zero randomly for missing data. Below is numbers 6-10 on the data set, the data has been cleaned up by changing the number 6 to the number 5 thus correcting the errors Sally made. Where a question wasn’t answered a zero was added to indicate missing data.

For defensive tackles broad jump and 40-yard dash showed the greatest negative correlation coefficient relationship because if the players perform poorly on a broad jump it means they would also perform slower in running 40-yard dash.

Data can be stored in data warehouses. However, they need data cleansing to standardise the data.

The quality of the study will depend on how carefully the researchers entered the data into software programs and verified its correctness. Verification can occur visually or by entering the data twice. Cleaning the data involves the identification of possible outliers by looking at the frequency distribution of the data in a graphical format, such as a scatterplot, followed by testing suspected outliers statistically. Visual inspection of the data should also reveal the presence of wild codes, which are values that make no sense. The data in its final form must therefore be verified for correctness and cleaned of outliers and wild codes.

Improper analyzed or interpreted data can lead the decision maker to the wrong conclusions or make decisions that are based on bias rather than facts; garbage in, garbage out.

11. Why is correlation data (data showing that two events occurred at the same time) not necessarily meaningful? Because one factor might not be the cause of the other.

When conducting a correlation study between exercise and water intake, the variables would be the amount of exercise and the volume of water consumption. There is a high possibility that there would be a positive correlation between the variables. Meaning, the more water the subject consumes, the longer that subject is able to exercise. There could be other variables that affect the results such as endurance, physical health, and energy. A subject that is more predisposed to put more effort into exercising would have more endurance compared to a subject that is not in prime physical

An example of a positive correlation is the use of manners and age. The use will increase as age does. They both increase in a consistent way.

This is a very important to the usability of the software. If the accuracy is off i.e. customer

Which of the following involves entering data in computer files, inspecting the data for errors, and running tabulations and various statistical tests? B. data analysis.

The correlation between Time 1 and Time 2 is 0.85 and is significant (0.000); however, if the reliability drops from 0.85 it must be decided if the test needs to be reexamined (Kline, 2005).

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The reality is that he world of big data is somewhat messy. Given data is collected so fast and is usually unprocessed (raw) it may be incomplete versus most traditional transactional data. Data may be missing many values or may contain incomplete records. Customer profiles may even be incomplete. Many companies embark upon a formal data cleansing strategy before or after placing data into their big data environment.