Bottle Company Case Study
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June 8, 2018
Bottling Company Case Study
1.Mean, Median, and Standard Deviation
Mean () = where xi is the volume in ounces, and n is the sample size in which is 30 in this case.
Thus, Mean () = 475.62 ÷ 30 = 15.854 ounces Median (M) = 50th percentile of the data
Arrange the data in ascending order, we have:
14.23, 14.32, 14.98, 15.00, 15.11, 15.21, 15.42, 15.47, 15.65, 15.74, 15.77, 15.80, 15 .82, 15.87, 15.98, 16.00, 16.02, 16.05, 16.21, 16.21, 16.23, 16.25, 16.31, 16.32, 16.34, 16.46, 16.47, 16.51, 16.91, 16.96
Since there are 30 values, implying that the data is even, to find the median, we choose the two middle values, i.e. 15.98 and 16.00 and
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Hence, we fail to reject the null hypothesis at 95% confidence level because -1.209 < -1.645. Hence the conclusion is that the average ounce is more than 16 ounces. Therefore, there is no sufficient evidence to support the claim that there are less than 16 ounces in a bottle of soda.
4. Since we failed to reject the null hypothesis, then, that the claim is not valid because there is not enough evidence to support the claim, then we can conclude that every soda bottle contains at least 16 fluid ounces on average. Since the only insignificant number of bottles that may be containing content that is less than the required 16 ounces, the management should consider it and rectify the condition.
The reasons that might have caused the bottles to contain less than the expected amount is:
i.Mechanical failure on the machines whereby some bottles might have taken a short time to be filled and thus resulting in less than enough content in the bottles. ii.Different bottle shapes in which case some might be having long bottlenecks while others short and others wider and narrower. In this case, the short neck will spill over some content thus reducing the volume, the wider bottlenecks will need more to be filled whereas the narrower ones take little hence the
so there isnt much left at the end. Finally, bottle 5 was different than the rest. We
I have four quiz scores: 94, 93, 85, and 0. what is the mean (average) of my quiz scores? Would you code this as a factor or a numeric value in R?
The first thing that was decided upon was to find the Mean, Median, and Mode. Using a calculator they were able to obtain the exact numbers.
In my experiment, I will use an overall volume of 50 cm³ of 2moles of
...will fall within the first standard deviation, 95% within the first two standard deviations, and 99.7% will fall within the first three standard deviations of the mean. The Empirical Rule is used in statistics for showing final outcomes. After a standard deviation is found, and before exact data can be collected, this rule can be used as an estimate to the outcome of the new data. This probability can be used for gathering data that may be time consuming, or even impossible to found. When the mean equals the median and the values cluster around the mean and median, producing a bell-shaped distribution, then we can use the empirical rule to examine the variability. In this bell-shaped data set, we can calculate the mean and the standard deviation. The mean means the average value of the set of data. The standard deviation means the average scatter around the mean.
Regarding the densities of Coke and Diet Coke, I believed that the density of coke would be greater than the density of Diet Coke. Because the content of Coke contains more sugar than Diet Coke, it would contain more mass and since density is mass dependent, Coke would be denser than Diet Coke. From the results of the experiment, there was a slight difference between the densities of Coke and Diet Coke. The measurements obtained from the pipette and the graduated cylinder demonstrated that Coke is denser than Diet Coke while Diet Coke was shown to be denser than Coke using the burette. With the pipette, the average density of Coke is 1.02 and the average density of Diet Coke is 0.99. With the graduated cylinder, the average density is 0.976968 and the average density of Diet Coke is 0.95. With the burette, the average density of Coke is 0.99 and the average density of Diet Coke is 1.0. Among the three instruments, the most precise was the graduated cylinder and the most accurate was the volumetric pipette. Since density is defined as mass/volume, changing the volume of Coke or Diet Coke would have changed.
The soft drink industry in the United States is a highly profitably, but competitive market. In 2000 alone, consumers on average drank 53 gallons of soft drinks per person a year. There are three major companies that hold the majority of sales in the carbonated soft drink industry in the United States. They are the Coca Cola Company with 44.1% market share, followed by The Pepsi-Cola Company with 31.4% market share, and Dr. Pepper/Seven Up, Inc. with 14.7% market share. Each company respectively has numerous brands that it sales. These top brands account for almost 73% of soft drink sales in the United States. Dr. Pepper/Seven Up, Inc. owns two of the top ten brands sold. Colas are the dominant flavor in the U.S carbonated soft drink industry; however, popularity for flavored soft drinks has grown in recent years. The changing demographics of the U.S population have been an important factor in the growing popularity of these flavored soft drinks. The possible impact of this factor will be addressed later in the case.
Based on the information and data collected through research the statement that soda has no nutritional benefits for the human body and causes harm, opposed to stating that soda has no negative effects to the body can be made. Soda is often a big part of a human’s diet. Therefore, the harmful effects of soda are occurring without people knowing soda is to blame and not having the knowledge of the illnesses that are linked to it.
0.000 7 63 106 55 74.7 1.245 9 70 135 90 98.3 1.638 11 85 135 70 96.8 1.613 [ IMAGE ] [ IMAGE ] Conclusion = = = =
Then, we multiply the result by 100 to get a percentage number. An example calculation from the 5.00mL pipet is: ((5.00mL - 4.9178mL)/5.00mL)*100 = 1.6438%. The percent error allows us to see which type of glassware is the most accurate at measuring substances. From our results, we concluded that the variable auto pipettor was the most accurate, since it had the lowest percent error, at 0.0500%. The volumetric pipet, volumetric flask, and burette, all had similar percent errors at 1.0430%, 0.6394%, and 0.6619%, respectively. These results indicate that these glassware types are still very accurate. Next, the graduated cylinder had a percent error of 1.6400%, which is still relatively low, indicating that the graduated cylinder is also very accurate. The beaker and erlenmeyer flask both had higher percent errors, at 7.5218%, and 9.4146%, respectively. The beaker and the erlenmeyer flask were not accurate at measuring substances. This is perhaps because they are larger, and are meant to hold larger volumes of water, or they are meant to contain and pour substances. After finding out
There is also the potential of human error within this experiment for example finding the meniscus is important to get an accurate amount using the graduated pipettes and burettes. There is a possibility that at one point in the experiment a chemical was measured inaccurately affecting the results. To resolve this, the experiment should have been repeated three times.
Experimentation with the new market for carbonated beverages on the decline coke has done experiments in new flavors and healthier alternatives to try to stay competitive. As well as investing in “Keurig Green Mountain is a K-Cup maker but has a new Keurig Cold that can deliver Coca-Cola through the new system.” (Cooper, 2014)
Less then 76% of people over the age of 2 have a glass a day but is that enough? (1)
Strom, Stephanie. "Bottled Water Sales Rising as Soda Ebbs." New York Times 25 Oct. 2013. Web. 8 Mar. 2014. .
Measures of central tendency are not the only statistics used to summarise a distribution . We also have to identify the spread of the distribution of the data set. Spread defines how widely the observations are spread out around the measure of central tendency. Note that the words, spread, dispersion and variation denote the same meaning. The most commonly used measures of spread are range, variance and standard deviation. The scales of measurement appropriate for the use of variance and standard deviation are ratio and interval scales.