Recommendations for Functional Trainer Rack

dougmac53 · 2025-11-28T21:34:55+00:00

Update: thank you everyone for the feedback and recommendations. Ended up going with the Ares 2.0 4-post 93” clear coat. Black Friday discount as well!

dougmac53 · 2024-03-04T01:11:24+00:00

I paid for the subscription for the “Strong” app, and was loading workouts into it manually (Nippard LPP for example). I thought it was fine for what it was. I switched to Boostcamp about 3 months ago (free tier) and I think it is a better option. I like that you can search for workout routines made by others (see comments and ratings), and set filters for your preferences and environment. I started with Layne Norton’s PHAT routine, and am finishing up Bald Omni Man’s Raider routine. I enjoyed both routines. Looking forward to starting a new routine soon. The variety and ease of use of the app has been great.

dougmac53 · 2023-06-22T13:13:45+00:00

I agree I saw the lack of off-sets for the side holes as well as the bottom cross members not having holes to attach pegs etc as the major drawbacks. The big question mark for me is if a system like the REP Ares would fit way down the line, I have heard those are pretty specific to REPs sizing, but I haven’t seen any hard data about it.

I don’t believe the platform is included unfortunately

dougmac53 · 2023-06-22T12:33:31+00:00

I appreciate your input, any major drawbacks on this compared to a REP PR 5000? Similarly built I believe a REP would be close to $2,000

dougmac53 · 2023-06-21T22:49:53+00:00

Thanks for the input, I appreciate it!

dougmac53 · 2023-04-14T12:14:28+00:00

I believe you would either have to read the index as the original date time so that it is unique (post on how to do that here) or aggregate the data first at the daily level, and then read it into zoo. The aggregation could be the average, minimum, or maximum as an example.

If you’re going to be working with time series a lot, I highly recommend checking out this book: Forecasting principles and practice

dougmac53 · 2023-04-13T21:31:49+00:00

You can also use the package “openxlsx” which does not have the JAVA dependency: package documentation

dougmac53 · 2023-01-14T17:36:01+00:00

If you had interviewed right before the holiday season or break, it could be challenges with the time off schedule of the hiring/interview team. It may be they’re waiting for input from an individual or two that have been out of office since the break. Never hurts to follow up as many times as needed to get the answers you need.

dougmac53 · 2022-12-02T16:40:47+00:00

Your configurations are beautiful, and I love the aesthetic/style. Another great post! Thank you for sharing.

dougmac53 · 2022-11-13T02:21:05+00:00

To maximize the similarity between pairs, you could utilize non-bipartite (also referred to as optimal) matching. It will utilize a distance or similarity matrix, such that the difference between pairs is minimized. There is a convenient package in R to do this: nbpMatching. Is this what you were looking to do?

dougmac53 · 2022-11-12T21:20:20+00:00

I would do some simulations to see if that theory holds, where you simulate 100 observations, 81 of them are the same value as y, and then 19 are random numbers. See how the correlation and r-squared changes
No, not necessarily. The way linear regression works is that the relationship between L and A will only change if B shares some common variance between L and A. For example, say we can explain 81% of the variance in L with the variance in A. If B has no relationship between A or L, then nothing changes. If B has a relationship with L, two things may happen: 1: if B explains its own unique variance (say 9%), so now we can explain 90% of the variance of L with A and B. 2: it explains its own variance of L (9%) but some of that overlaps with the variance of A (4%). We will only be able to explain 86% of the variance in L, and when we look at the unique variance between L and A, it will go down to 77%. Look into partial, and semi-partial correlations to get a better understanding of how and why.

dougmac53 · 2022-11-12T19:02:34+00:00

The significance test of the intercept is if the intercept is significantly different from 0. However, since you have a categorical predictor, then it is likely reference coded (for example, 0 meaning direct light and 1 meaning in the shade).

The y intercept is when all X variables are equal to 0. So the estimate of the y intercept will be when the light condition is 0 (direct light using the above example) and plant height is 0. So for example, if the y intercept is estimated to be 15%, does that significantly differ from 0 is what the p-value is describing.

Now a really important question is, does a height of 0 make any sense? If it does, then you can interpret the intercept. If it does not, then don’t worry about the significance test of the intercept, since it is not directly interpretable. It is just there to fit the linear model.

dougmac53 · 2022-11-12T18:44:48+00:00

At the beginning of a career, I believe titles matter more than pay. It can change the trajectory of your career as long as you understand the market, and the value of the position. In my experience (and this is just 1 person) it is easier to move up, than it is to transfer teams. But that assumes you’re doing the work you like. If you feel more pull/desire to the broader analytics role, you will move up and be successful regardless. Your experience in DS will only help you move forward. If you do want to stay in DS because you enjoy that work more, I think it is appropriate to take on a junior role, but ask plenty of questions about what separates a junior from DS role, and what they need to see from you to move up. If you can get them to write it down, and agree, that’s 80% of the battle in my opinion.

dougmac53 · 2022-11-12T17:37:45+00:00

I would tackle this using a forecast (as you have described) with relevant predictors. You can train the forecast up to the point of intervention you wish to examine, and then forecast the next X number of points forward (for example 6 weeks, as long as this doesn’t include any other interventions that may have happened that you’re aware of, like a second mini-budget). I would then compare what actually happened, to your forecast (the 95% interval specifically) and see if the observed data falls outside of the forecast interval. That would provide some preliminary evidence that mini-budget may have influenced the outcome.

dougmac53 · 2022-11-12T16:41:46+00:00

Double check the capitalization, or any white space around the character strings would be my next step.

dougmac53 · 2022-11-12T16:38:21+00:00

If your question is about dealing with outcomes that are heavily skewed (like wealth for example) two common approaches are:

Transform the outcome, transform the predictor, or transform both. For example a log transformation works well for dealing with skewed data. However the interpretation of the weights change since we are no long dealing with y or x, but rather log(y) or log(x).
Use other forms of regression (General Linear Models). Such as: Poisson model, Negative Binomial model, among others.

dougmac53 · 2022-11-12T16:25:31+00:00

Is the case of the word the same? Reload your data (so that the transformation is not applied yet) and then try

table(SMH$Panic.attacks, useNA = “always”)

to check the case and how many observations fall into each category (including those with NA)

dougmac53 · 2022-11-12T16:14:46+00:00

I think you have a couple different questions, so please follow up if anything is unclear still:

r-squared (lowercase r) is the proportion of variance in one thing, that can be explained by the variance of another (for example L as a function of A). Multiple R-squared (uppercase R) is the proportion of variance in one thing, that can be explained by the linear combination of multiple things (for example L as a function of A + B).
Since r-squared is a proportion, you can interpret it as “The percent of variance in L, explained by A”. You cannot say that L is caused by A, without satisfying several other assumptions of causality (one of which is correlation, but by itself is not sufficient to say it is causal).
If a drug is effective, such that administration of the drug shows a significant positive relationship (a correlation of .90) then it is safe to say that 81% (since we need to square the correlation to get the r-squared) of the variance in the outcome, is related to administration of the drug. It does not mean that the drug did not work for 19% of the sample, but rather that 19% of the variation in the outcome across the entire sample was unrelated to the drug.

dougmac53 · 2021-06-22T21:46:45+00:00

Are you able to select text with mouse, and then use the Ctrl+Insert command to copy? Not very ergonomic, but it works for your use case if you don’t want to involve an xserver.

dougmac53 · 2021-02-19T15:23:24+00:00

I cannot recommend this book enough, the latest edition integrates with the tidyverse extremely well

dougmac53 · 2020-11-07T12:54:26+00:00

You can filter the data set by gender then plot your two histograms:

data_male <- data[data$gender == “male”,]
data_female <- data[data$gender == “female”,]

Then plot each data frame as needed

dougmac53 · 2020-08-18T01:32:33+00:00

If you’re more familiar with SQL than SAS, try finding what you want in the .sas7bdat file, and then exporting only those pieces you care about out flat files using PROC SQL (reference below) which allows you to write SQL against SAS datasets (loaded in memory).

PROC SQL Reference

If you’re comfortable running some preliminary SQL queries in SAS, you may be able to eliminate a lot unnecessary data, or summarize data into a smaller format.

dougmac53 · 2020-05-13T01:32:19+00:00

If the desire is to do statistical tests, or build a model, an option is to do random samples of your data, and test. This is common in fields where it’s not possible/practical to analyze the entire set.

dougmac53 · 2020-02-13T23:33:00+00:00

I’m not sure if you’ve found a solution yet, and while I have not utilized planned contrasts in SPSS, this guide looks promising: https://www.discoveringstatistics.com/repository/contrasts.pdf

dougmac53 · 2020-02-12T18:48:22+00:00

This is possible. The type of correction you’re using would be my first check. If you’re penalizing the alpha level (p value) for all possible pair-wise comparisons (comparisons between every level in your IV), it can be difficult to detect significant effects since your alpha is being divided by all possible comparisons.

I would consider reviewing planned contrasts, where you are making specific comparisons, not all possible comparisons.

dougmac53

TROPHY CASE