We Can Then Continue For Graphing

* Given linear and exponential data, interpret the rate of change within the given context.

Slide 16: This curve demonstrates a one-tail hypothesis with the critical region representing 5% showing a positive relationship.

In the following graph, the exponential function has a very similar trend to the trend of y=2x. It is important to note that the exponential function is actually y=ax where a > 0. Using y=1x would only produce a horizontal line. Therefore the next value used was y=2x. In order to make the function fit better with the points we make transformations that slightly change the function. The first step is to lower the line so that the line can become closer to the data points. With this data set it sloa stands to reason that there are negative numbers very much possible. It would work in the exponential equation 2x-1.

This type of chart is used when one wants to identify the total number of defects that have occurred during a sampling period. The number of samples that occur during the period are essentially the same. This chart has the number of defects on the vertical axis and the number of samples (weeks) on the horizontal axis. The sample size for this problem is over a period of 20 weeks, where a new process was implemented at week 11. Since a new plan was implemented, it caused the two different graphs to appear. The first graph, which occurs before week 11, has a UCL of 28.4 and an average of 16.3. A UCL stands for an upper control limit, which is generally 3 standard errors from the median, and is always the top line of the graph. The median is used as the centerline of the graph. An LCL is apparent in the graph (bottom line), but no exact value is given. The points that are shown on the graph are the “statistical measurement samples taken from the process at different times” (Control Charts PPT., 25, 2016). The second graph, which occurs after week 11, has a UCL of 7.4 and an average of 2.6. This subgroup is also complete with an upper UCL line and a median centerline. After week 11, a new plan was implemented into the hospital to reduce the number of defects. The plan that they used decreased the total number of defects, decreased the UCL, average, and LCL. A strategy that may have been able to reduce the amount of defects was Six Sigma. Six Sigma would work for this type of scenario because it measures how many defects exist in a process and you can figure out how to eliminate them all

The average rate of change is an average slope from the initial point to the final point. We would have to use A(x)=f(x)-f(a) divided by x-a to find the average rate of change. Sometimes we would want to know what the average rate of change is in the middle of the graph. The average rate of change is used in page 10, “How many more people?.” The concept of slope comes from the idea of a constant rate of change. To find the slope, you have to calculate y1-y2 divided by x1-x2. Slope is often denoted by the letter “m” which means that m+ equals the slope. Its problem is being used in page 21, “Rates, Graph, Slopes, and Equations.” Y=mx+b is the equation of the line that you can find using the two points. Variable “m” is the slope and “b” is the y-intercept.

Person 1 has longer ventricular filling time because they have a lower heart rate which causes the ventricles to take longer to fill up with blood. Venous return is the amount of blood returning to the heart. Since person 2 has a greater stroke volume, Person 2 has the greater venous

The line graph was introduced at the end of third grade, but not in much detail. In this edition, the line graph is used to display a person's height from birth to 10 years of age.

6. Why is the black line so much more variable than the red line? What 's the difference between the data they show?

Initially, 8 pennies were added to the cup, followed by the addition of 7 pennies and 1 dime, then 4 pennies and 4 dimes, and finally 8 more pennies. There were therefore a total of 27 pennies and 5 dimes added to the cup. Table 2 demonstrates that the force (N) for dimes and pennies went up by almost 0.20 N at each interval. Therefore, the force (N) in Table 2 did not deviate much from the force (N) seen in Table 1 where all pennies were used. The reason little variation in force was seen in Table 2 was due to mostly pennies being added to the cup. Due to so many pennies being added, the dimes had little impact on the overall force (N). If roughly an equal ratio of pennies to dimes had been added to the cup, a more distinct variation between the slope’s in Figure 1 and Figure 2 would have been seen. However, the slope or the average weight of the coins, as represented in Figure 2, was 0.0249 N. The slope can be calculated by dividing the change in the force by the change in the # of pennies and dimes. The x-values represent the number of pennies and dimes, while the y-values represent force (N). The y-intercept value is equal to 0, therefore, the linear equation is y=0.0249x+0. After plotting the line on a manual curve fit, as can be seen in Figure 2, the R2 value was 0.99884. This R2 value is very close to 1, meaning that the match of the linear model to the data fits. After running a

To do this plot, it was took the reciprocal of everything. The reciprocal formulas used was “=1/”. It was selected the cell and typed the formula, and then selected the cell need it to do reciprocal of. It was started with B40, so it was like “=1/B40”. Since data was organized, it was used the black cross again to spread the formula around the other cells in the data set. It was repeated the same produce for I=5, 15, and 20 and for Inhibitor 2, and 3 to yield all to have the double reciprocal data. Finally, by doing these 3 graphs, can be calculated the Km and Vmax for each inhibitor concentration. To allow to do this, it was selected Chart, but now it was used Marked Scatter plots because this does not allow to connect the points. After, it was added the four inhibitor concentrations, added labels to the X and Y-axis, given a title, and selected Add Trendline. Then, it was selected “Display equation on the chart” and under Forescast was changed into 0.1 Periods in Backwards. This allowed to see where the X and Y-axis hit, and allow to determine the type of

Health economics is the discipline of economics applied to the topic of health care. Broadly defined, economics concerns how society allocates its

Resource based relative value scale (RBRVS) was designed to determine the cost of the medical providers and the amounts they should be paid. It is ususally used and created by the medicare in the US and by nearlly all HMOs. It assigns procedures performed by the physician or the medical providers. The RBRVS will determine its prices based on some factors such as the physicians work 54%, practices expense 41%, and the malpractice expenses to a 5%. It can relate to insurance through making or deciding o how to pay the physician for their services provided to the patient. With this system the physicians services will be determined based on the RBRVS and payments will be calculated using this method. Both the RBRVS factors and relative values

This regression equation can be graphed as follows assuming β0 as the intercept and β1 as the slope:

On the graph above you can see that both the quadratic and the line are both adequate representations of the data collected by gold medalists for the men’s high jumps in the Olympics. Both of these lines follow the plots made on the original graph and they don’t stray too far from those lines either. There only outlier for the quadratic seem to be the medalist from the 1948 Olympics because his height is far below the quadratic. There might be some problems with the exact position of where the quadratic is and where the line is because they were drawn by hand and not on the computer like the stat plots which could potentially cause problems for interpreting the data.

The refined equation fits the data much more closely at the beginning, yet around the Age = 13 mark it starts to deviate again. Looking at this graph, one can come to the conclusion that it will take more than one mathematical function to graph the entirety of the data.