I have a question on checking if two values are statistically significant. I looked at t-test, but not sure if it applies here.
The data I have is total car accidents (just total values) for 4 years 2016 - 2019 and the total number of preventable car accidents for 2016 - 2019. Is there a statistical test to check if the change between these preventable car accidents values over the years is statistically significant?
Related
First of all I'd like to make it clear, that I'm a total newbie to MDX and only grasp the absolute basics. I'll do my very best to try and describe my issue, and hope that some of you guys have time to help me. If it's to much to ask for, I totally understand, but not putting my question out there wouldn't get me anywhere either way, right.
I'm working with and Excel Olap Pivot table, and I'm looking to create a new MDX measure that multiplies the sum of expenditure in each of the last five years by a factor, like in the example below:
Year
Expediture
Factor
2018
100.000
1.05
2019
100.000
1.04
2019
100.000
1.03
2020
100.000
1.02
2021
100.000
1.01
I have an Excel Olap Pivot table with the measure [Measures].[Expected Expenditure] and the dimension [Date].[Year].[Year]). The dimension "[Date].[Year].[Year])" also holds data for other years than the five years I need to weight the expenditure in. The Factor is a set number and I'm looking to hard-code that into the calculation.
How do I go about creating a new MDX measure that weights the expenditure in each of the five years, but doesn't add a weight to the expenditure in the years outside the scope?
Please let me know if the above description is deficient or if there's anything I need to clarify further. English is not my native tongue and I apologize for anything incoherent writing.
Best regards,
Magnus
Currently I have a grouped bar chart with a value on the y-axis (total guest nights in an area), and the months on the x-axis.
I have 2 series as of now: 2020 and 2019 - they are quite close to each other for each months so that you see that these relate to one another.
NOW I wanted to break each of these series into a more detailed aggregate.
The total guest nights each bar represent I now want the bar to aggregate the value for each market - whether domestic or abroad (so 2 components should now constitute the earlier total sum)
My idea doing this was (which I know work if I had just 1 year - e.g. 2019), is to put the e.g. abroad as the total amount and the domestic as domestic - then just use 100% series overlap.
BUT if I do this now - of course this happens to both my series related to e.g. 2019 but also for 2020 (which I do not want)..
I tried making use of the secondary y-axis for the 2020 series - but that didn't help at all - Excel still relate series overlap (on the x-axis) the same regardless of me making use of the 2nd-y-axis.
Do you understand my question?
I essence I want a stacked bar chart for 2 series (or in practicality 4).
Thanks!
I am new to spatial statistics. I am working on a project analyzing the spatio-temporal neonatal mortality. I have data for six year from 2012 to 2017 specified at district level per month.
How can I compare the spatial variation of neonatal mortality rate across the 6 years. I would want to be able to explain statistically the changes of the neonatal mortality rate either for each district or the country as a whole for the 6 year period.
Any assistance would be greatly appreciated. Thanks!
Below is the brand preference metric data for two overlapped time periods (Week 1-3 and Week 2-4 data). I wanted do a significance test between these two overlapped groups, please provide me the details of the method and formula’s to use. We are doing these activities in excel format.
Here is the my data :
Roll Up Week 1-3 Roll Up Week 2-4
Count 697 706
Brand Preference 43% 40%
Thanks,
Tanuvi
I am trying to figure out what the optimal number of products I should make per day are, displaying the values in a chart and then using the chart to find the optimal number of products to make per day.
Cost of production: $4
Sold for: $12
Leftovers sold for $1
So the ideal profit for a product is $8, but it could be -$3 if it's left over at the end of the day.
The daily demand of sales has a mean of 150 and a standard deviation of 30.
I have been able to generate a list of random numbers using to generate a list of how many products: NORMINV(RAND(),mean,std_dev)
but I don't know where to go from here to figure out the amount sold from the amount of products made that day.
The number sold on a given day is min(# produced, daily demand).
ADDENDUM
The decision variable is a choice you make: "I will produce 150 each day", or "I will produce 145 each day". You told us in the problem statement that daily demand is a random outcome with a mean of 150 and a SD of 30. Let's say you go with producing 150, the mean of demand. Since it's the mean of a symmetric distribution, half the time you will sell everything you made and have no losses, but in most of those cases you actually could have sold more and made more money. You can't sell products you didn't make, so your profit is capped at selling 150 on those days. The other half of the time, you won't sell all 150 and will take a loss on the unsold items, reducing your profit a bit. The actual profit on any given day is a random variable, because it is determined by random demand.
Since profit is random, you can calculate your average earnings across many days based on the assumption that you produce 150. You can also average earnings based on the assumption that you produce 140 per day, or 160 per day, or any other number. It sounds like you've been asked to plot those average earnings versus how many you decided to produce, and choose a production level that results in the highest long-term average earnings.